Adaptive Quantization

Rate control typically involves two steps: bit allocation and bitrate control. The bit allocation step can be implemented in various fashions depending on how many levels of allocation are desired and whether or not an optimal ratedistortion (R-D) performance is pursued. The bitrate control step has a simple aim in achieving the target bitrate as precisely as possible. In our recent research, we have developed a λ-domain rate control algorithm capable of controlling the bitrate precisely for High Efficiency Video Coding (HEVC). The initial research [1] showed that the bitrate control in the λ-domain can be more precise than the conventional schemes. However, the simple bit allocation scheme adopted in this initial research is unable to achieve an optimal R-D performance reflecting the inherent R-D characteristics governed by the video content. In order to achieve an optimal R-D performance, the bit allocation algorithms need to be developed taking into account the video content of a given sequence. The key issue in deriving the video content-guided optimal bit allocation algorithm is to build a suitable R-D model to characterize the R-D behavior of the video content. In this research, to complement the R-λ model developed in our initial work [1], a D-λ model is properly constructed to complete a comprehensive framework of λ-domain R-D analysis. Based on this comprehensive λ-domain R-D analysis framework, a suite of optimal bit allocation algorithms are developed. In particular, we design both picture level and Basic Unit level bit allocation algorithms based on the fundamental rate-distortion optimization (RDO) theory to take full advantage of content-guided principles. The proposed algorithms are implemented in HEVC reference software, and the experimental results demonstrate that they can achieve obvious R-D performance improvement with smaller bitrate control error. The proposed bit allocation algorithms have already been adopted by the Joint Collaborative Team on Video Coding (JCT-VC) and integrated into the HEVC reference software.

In recent years, quantum image processing got a lot of attention in the field of image processing due to the opportunity to place huge image data in quantum Hilbert space. Hilbert space or Euclidean space has infinite dimensions to locate and process the image data faster. Moreover, several types of research show that the computational time of the quantum process is faster than classical computers. Encoding and compressing the image in the quantum domain is still a challenging issue. From the literature survey, we have proposed a DCT-EFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) algorithm to represent and compress gray image efficiently which save computational time and minimize the complexity of preparation. This work aims to represent and compress various gray image sizes in quantum computers using the DCT (Discrete Cosine Transform) and EFRQI (Efficient Flexible Representation of Quantum Image) approaches together. The Quirk simulation tool is...

In this paper, a lossy image compression is introduced, it based on utilizing three techniques of wavelet, polynomial prediction and block truncation coding, in which each technique exploited in away according to redundancy presents. The test results shown are promising performance in terms of higher compression performance achieved with lower noticeable error or degradation.

Recent advances in digitization of geometry and radiometry generate in routine massive amounts of surface meshes with texture or color attributes. This large amount of data can be compressed using a progressive approach which provides at decoding low complexity levels of details (LoDs) that are continuously refined until retrieving the original model. The goal of such a progressive mesh compression algorithm is to improve the overall quality of the transmission for the user, by optimizing the rate-distortion trade-off. In this paper, we introduce a novel meaningful measure for the cost of a progressive transmission of a textured mesh by observing that the rate-distortion curve is in fact a staircase, which enables an effective comparison and optimization of progressive transmissions in the first place. We contribute a novel generic framework which utilizes the cost function to encode triangle surface meshes via multiplexing several geometry reduction steps (mesh decimation via half-edge or full-edge collapse operators, xyz quantization reduction and uv quantization reduction). This framework can also deal with textures by multiplexing an additional texture reduction step. We also design a texture atlas that enables us to preserve texture seams during decimation while not impairing the quality of resulting LODs. For encoding the inverse mesh decimation steps we further contribute a significant improvement over the state-of-the-art in terms of rate-distortion performance and yields a compression-rate of 22:1, on average. Finally, we propose a unique single-rate alternative solution using a selection scheme of a subset among LODs, optimized for

Wavelet-based resolution enhancement methods improve the image resolution by estimating the high-frequency band information. In this paper, we propose a new resolution enhancement method using inter-subband correlation in which the sampling phase in DWT is considered. Interpolation filters are designed by analyzing correlations between subbands having different sampling phases in the lower level, and applied to the correlated subbands in the higher level. The filters are estimated under the assumption that correlations between two subbands in the higher level are similar to that in the lower level in DWT. The experimental results show that our proposed method outperforms the conventional interpolation methods including the other wavelet-based methods with respect to peak signal-to-noise-ratio (PSNR) as well as the subjective quality.

In this paper, we propose an invisible blind watermarking scheme for the graylevel images. The cover image is decomposed using the Discrete Wavelet Transform with Biorthogonal wavelet filters and the watermark is embedded into significant coefficients of the transformation. The Biorthogonal wavelet is used because it has the property of perfect reconstruction and smoothness. The proposed scheme embeds a monochrome watermark into a gray-level image. In the embedding process, we use a localized decomposition, means that the second level decomposition is performed on the detail sub-band resulting from the first level decomposition. The image is decomposed into first level and for second level decomposition we consider Horizontal, vertical and diagonal subband separately. From this second level decomposition we take the respective Horizontal, vertical and diagonal coefficients for embedding the watermark. The robustness of the scheme is tested by considering the different types of image...

This paper discusses some key elements and solutions for efficient video compression. More precisely, we introduce key differentiating techniques for video signal quantization within modern hybrid video codecs. First, we recall the fundamentals about scalar quantization and Rate-Distortion (R-D) theory, then describe the different control points and levels of granularity for optimizing signal quantization into modern video compression standards. From this common knowledge, we discuss approaches in how to jointly optimize those various levels of quantization refinements for improved video encoding quality. Most notably, we explain how to take advantage of a lookahead module (available in most of industrial encoder implementations) to model spatio-temporal coding dependencies, and how to further compute optimal quantization information from an R-D standpoint for a group-ofpictures (GOP). Complementary to this first approach, we share some insights about a Local Quantization Refinement (LQR) algorithm. Such algorithm is often ignored in practical encoder implementation due to its apparent complexity; we develop how and why it can efficiently work in realtime software encoding.

Hybrid video coding systems use spatial and temporal predictions in order to remove redundancies within the video source signal. These predictions create coding-schemerelated dependencies, often neglected for sake of simplicity. The R-D Spatio-Temporal Adaptive Quantization (RDSTQ) solution uses such dependencies to achieve better coding efficiency. It models the temporal distortion propagation by estimating the probability of a Coding Unit (CU) to be Inter coded. Based on this probability, each CU is given a weight depending on its relative importance compared to other CUs. However, the initial approach roughly estimates the Inter probability and does not take into account the Skip mode characteristics in the propagation. It induces important Target Bitrate Deviation (TBD) compared to the reference target rate. This paper provides undeniable improvements of the original RDSTQ model in using a more accurate estimation of the Inter probability. Then a new analytical solution for local quantizers is obtained by introducing the Skip probability of a CU into the temporal distortion propagation model. The proposed solution brings −2.05% BD-BR gain in average over the RDSTQ at low rate, which corresponds to −13.54% BD-BR gain in average against no local quantization. Moreover, the TBD is reduced from 38% to 14%.

Nowadays, Rate-Distortion Optimization (RDO) is commonly used in hybrid video coding to maximize coding efficiency. Usually, the rate distortion tradeoff is explicitly computed in offline encoder implementations whereas R(D) model are used in live encoders to select the best decisions at a lower computational cost. For sake of simplicity, this (mathematical) modelling is often performed for each coding unit (CU) individually and independently, obliterating the spatial or temporal dependency between CUs. In this paper, we provide a new spatio-temporal algorithm to compute local quantizers, based on a theoretical framework able to describe the temporal distortion propagation from an R-D standpoint. In particular, we model the temporal distortion propagation making possible the retro accumulations of any (spatial) psycho-visually weighted distortion onto reference images. Using the R(D) Shannon bound, its high bitrate approximation, and a Lagrange optimization, analytical solutions are obtained for the local quantizers and the Lagrange multiplier. The proposed algorithm shows-4.4% BD-BR SSIM gains in average over state-of-the art algorithm in HEVC, using the same SSIM-based psycho-visual function.

Optimal adaptive quantization is one of the key points to optimize the coding efficiency of video encoders. Latest block-based video compression standards, such as High Efficiency Video Coding (HEVC), extensively use predictive coding techniques that create dependencies between blocks and increase the complexity of optimal block quantizers search. Specifically, the motion compensation is responsible for a dependency network connecting all blocks of the same GOP together. In this paper, this dependency network is estimated by a temporal distortion propagation model and an accurate estimation of Inter and Skip modes probabilities. Optimal quantizers are then designed per block in order to achieve the global optimization in terms of Rate-Distortion efficiency. By implementing the algorithm into the HEVC reference Model (HM), we report −16.51% PSNR-based and −26.26% SSIM-based average bitrate savings compared to no adaptive quantization. The proposed algorithm outperforms several related methods from state-of-the-art. Moreover, along with the demonstration of optimal quantizer solution, we propose an in-depth analysis of the algorithm behavior. This analysis includes, among others, the relative distribution of rates between frames and the control of quantizers dynamic range.

Stereoscopic and multiview imaging techniques are used for reproducing a natural or real world scene. However, the fact that more information is displayed requires supporting technologies to ensure the storage and transmission of the sequences. Beyond these supports comes watermarking as a desirable alternative solution for copyright protection of stereo images and videos. This paper introduces a watermarking method applied to stereo images in wavelet domain. This method uses a particle swarm optimization (PSO) evolutionary computation method. The aim is to solve computational complexity problems as well as satisfy an execution time that complies with normal PCs or smart phones processors. Robustness against image attacks is tested, and results are shown.

In the paper is presented a new method for efficient compression of still images, based on the Inverse Difference Pyramid (IDP) decomposition. A special version of the method is developed for the lossless compression of graphics and texts. The method is aimed at secure and flexible transfer of information via Internet. It permits the insertion of multiple digital watermarks in

Watermarking is a method to hide the image efficiently into any covering object (image in our case) so no intruder can interpret it by any means. Proposed work is a new design of image watermarking which include pre-processing of cover image with Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). Proposed work using an alphanumeric key which initially modifies the watermark using simple ‘XOR’ operation, and at the receiver end this key must be there so that receiver can extract the watermark.  Proposed work is also using torus Automorphism which initially changes the watermark into a scramble format which cannot be recognised as original watermark.

The mobile network security is becoming more important as the number of data being exchanged on the internet increases. The growing possibilities of modern mobile computing environment are potentially more vulnerable to attacks. As a result, confidentiality and data integrity becomes one of the most important problems facing Mobile IP (MIP). To address these issues, the present paper proposes a new Wavelet based watermarking scheme that hides the mobile signals and messages in the transmission. The proposed method uses the successive even and odd values of a neighborhood to insert the authenticated signals or digital watermark (DW). That is the digital watermark information is not inserted in the adjacent column and row position of a neighborhood. The proposed method resolves the ambiguity between successive even odd gray values using LSB method. This makes the present method as more simple but difficult to break, which is an essential parameter for any mobile signals and messages. To test the efficacy of the proposed DW method, various statistical measures are evaluated, which indicates high robustness, imperceptibility, un-ambiguity, confidentiality and integrity of the present method.

In this paper, a fully automatic scheme for hiding digital watermarks into face regions is proposed. To achieve this goal, an adaptive two-dimensional Gaussian model of skin color distribution is initially used in order to detect face regions within the initial image. Next each face region is decomposed into three levels with ten subbands, using the Discrete Wavelet Transform (DWT) and three pairs of subbands are formed (HL3, HL2), (LH3, LH2) and (HH3, HH2). Afterwards Qualified Significant Wavelet Trees (QSWTs), which are derived from the Embedded Zerotree Wavelet (EZW) algorithm and they are high-energy coefficient paths, are estimated for a pair of subbands. Finally visually recognizable watermark patterns are redundantly embedded to the coefficients of the highest energy QSWTs and the IDWT is applied to provide the watermarked face area. Performance of the proposed face region watermarking system is tested under various signal distortions such as JPEG lossy compression, sharpening and blurring. Experimental results on real life images indicate the efficiency and robustness of the proposed scheme.

One of the problems of conventional visual quality evaluation criteria such as PSNR and MSE is the lack of appropriate standards based on the human visual system (HVS). They are calculated based on the difference of the corresponding pixels in the original and manipulated image. Hence, they practically do not provide a correct understanding of the image quality. Watermarking is an image processing application in which the image's visual quality is an essential criterion for its evaluation. Watermarking requires a criterion based on the HVS that provides more accurate values than conventional measures such as PSNR. This paper proposes a weighted fuzzybased criterion that tries to find essential parts of an image based on the HVS. Then these parts will have larger weights in computing the final value of PSNR. We compare our results against standard PSNR, and our experiments show considerable consequences. Keywords—HVS, watermark, imperceptibility, weighted

One of the problems of conventional visual quality evaluation criteria such as PSNR and MSE is the lack of appropriate standards based on the human visual system (HVS). They are calculated based on the difference of the corresponding pixels in the original and manipulated image. Hence, they practically do not provide a correct understanding of the image quality. Watermarking is an image processing application in which the image's visual quality is an essential criterion for its evaluation. Watermarking requires a criterion based on the HVS that provides more accurate values than conventional measures such as PSNR. This paper proposes a weighted fuzzybased criterion that tries to find essential parts of an image based on the HVS. Then these parts will have larger weights in computing the final value of PSNR. We compare our results against standard PSNR, and our experiments show considerable consequences. Keywords—HVS, watermark, imperceptibility, weighted

This paper introduces a new adaptive algorithm for digital watermark embedding in wavelet domain. The proposed algorithm performs adaptive mother wavelet synthesis based on a low frequency component energy maximization. The algorithm is based on an orthogonal neural network. We demonstrate that the presented adaptive method can improve both the correlation between an extracted watermark and an embedded watermark, as well as the fidelity of an image. The proposed algorithm is applied to improve well known wavelet based embedding algorithms.

Hybrid video coding systems use spatial and temporal predictions in order to remove redundancies within the video source signal. These predictions create coding-schemerelated dependencies, often neglected for sake of simplicity. The R-D Spatio-Temporal Adaptive Quantization (RDSTQ) solution uses such dependencies to achieve better coding efficiency. It models the temporal distortion propagation by estimating the probability of a Coding Unit (CU) to be Inter coded. Based on this probability, each CU is given a weight depending on its relative importance compared to other CUs. However, the initial approach roughly estimates the Inter probability and does not take into account the Skip mode characteristics in the propagation. It induces important Target Bitrate Deviation (TBD) compared to the reference target rate. This paper provides undeniable improvements of the original RDSTQ model in using a more accurate estimation of the Inter probability. Then a new analytical solution for local quantizers is obtained by introducing the Skip probability of a CU into the temporal distortion propagation model. The proposed solution brings -2.05% BD-BR gain in average over the RDSTQ at low rate, which corresponds to -13.54% BD-BR gain in average against no local quantization. Moreover, the TBD is reduced from 38% to 14%.

Stars form out of the gravitational collapse of centrally condensed cores of dense molecular gas. Systematic observations of molecular emission lines which are excited at the high densities and cold temperatures found in star forming regions have revealed the physical and chemical structure of solar-mass size cores which have formed or may soon form a single star or stellar binary, and form the basis of theories of single-star formation. The initial conditions of star formation in this `isolated' mode have consequently become well-understood. Most stars, however, form in more clustered environments. With the goal of characterizing the physical conditions of dense gas in cluster-forming environments, we present the results of systematic mapping of expected dense gas tracers (including NH3, N2H+, N2D+ and H2D+) towards the small cluster-forming Ophiuchus B Core. We find strong evidence that NH3 and N2H+ do not trace the coldest and densest locations of the core, including signifi...

In recent advancements in the information technologies, a new set of challenging problems regarding security is increased.most of the industries providing security to their own as well as hired/leased data. Digital watermarking has provided a powerful way to claim intellectual protection. Generally, watermarking plays a pivotal role to provide the security on authorised access. The main scope of this paper is to study the multiresolution watermarking algorithms and also choosing the effective and efficient algorithm which is improving the resistance to data compression. This paper proposed a new idea for enhancing the robustness of extracted watermarks. Watermark can be treated in this research work as a transmitted signal, while the destruction from attackers is regarded as a noisy distortion in the channel. Two various watermarking algorithms can be taken for this paper namely Corvi and Wang algorithm. In all graph, we have plotted X axis as peak signal to noise ratio (PSNR) and Y...

To reduce the detection failure of the exchanging signal power onto the OFDM subcarrier signal at uniform quantization, dynamic subcarrier mapping is applied. Moreover, to addressing low SNR’s wall-less than pre-determine threshold, non-uniform quantization or adaptive quantization for the signal quantization size parameter is proposed. μ-law is adopted for adaptive quantization subcarrier mapping which is deployed in mobility environment, such as Doppler Effect and Rayleigh Fading propagation. In this works, sensing node received signal power then sampled into a different polarity positive and negative in μ-law quantization and divided into several segmentation levels. Each segmentation levels are divided into several sub-segment has representing one tone signal subcarrier number OFDM which has the number of quantization level and the width power. The results show that by using both methods, a significant difference is obtained around 8 dB compared to those not using the adaptive m...

In this paper, we propose a perceptual adaptive quantization based on a deep neural network on high efficiency video coding (HEVC) for bitrate reduction while maintaining subjective visual quality. The proposed algorithm adaptively determines frame-level QP values for different picture types of the hierarchical coding structure in HEVC by taking into account the high-level features extracted from the original and previously reconstructed pictures. A predefined model based on the visual geometry group (VGG-16) network is exploited to extract the high-level features for subjective visual characteristics. Furthermore, the Lagrange multiplier for each frame is also adaptively determined by involving the proposed features for deciding the appropriate parameter of the Lagrange multiplier that can be used for rate-distortion optimization during the encoding process. Experimental results reveal that the proposed perceptual adaptive QP selection can facilitate bitrate savings up to 65.73% and 47.68% and improve the BD-rate based on SSIM by approximately 20.68% and 14.27% under low-delay-P and random-access coding structures, respectively, with very minimal visual quality degradation when compared to HM-16.20 without adaptive QP selection. INDEX TERMS Adaptive quantization parameter, deep neural network, high efficiency video coding (HEVC), perceptual quantization parameter, VGG-16 network, video coding.

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it is shown first that quadrature filters (QFs) and wavelet-generated filters designed for an exact reconstruction of an infinite signal, in a subband coding system, induce a reconstruction error at the picture boundaries. This error is evaluated. Then, a new image coding system is proposed in which sampled images are decomposed into subbands in a one-step pr ~cedure. Ideal filters are used and an error-free reconstruction is achieved. Filters are implemented with FFT. A few overh~.ad bits are necessary. The c, omputer load is evaluated and compared with the pyramidal subband coding with FIR QM Fs. lllu,'trative examples allow a comparison of the proposed method with subband coding using QMFs and show clearly the PPSNR gain in the reconstructed images.

Insertion of a text message, audio data or/and an image into another image or 3D model is called as a watermarking process. Watermarking has variety of applications like: Copyright Protection, Owner Identification, Copy Protection and Data Hiding etc., depending upon the type of watermark insertion algorithm. Watermark remains in the content after applying various attacks without any distortions. The blind watermarking method used in the system is based on a wavelet transform, a fuzzy inference system and a multiresolution representation (MRR) of the 3d model. The watermark scrambled by Arnold Transform is embedded in the wavelet coefficients at third resolution level of the MRR. Fuzzy logic approach used in the method makes it to approximate the best possible gain with an accurate scaling factor so that the watermark remains invisible. The fuzzy input variables are computed for each wavelet coefficient in the 3D model. The output of the fuzzy system is a single value which is a perceptual value for each corresponding wavelet coefficient. Thus, the fuzzy perceptual mask combines all these non-linear variables to build a simple, easy to use HVS model. Results shows that the system is robust against affine transformations, smoothing, cropping and noise attacks.

With the advent of internet and high speed networks it become easy and simple to share multimedia data over internet .Internet downloading is also easily achievable. It poses threat to copyright data. As it enhances the illegal copying, modification of content and distribution of data. In this situation there arises a need to secure the original digital content from unauthorized access, duplication and tampering of data. Digital watermarking is the technique which serves the purpose of copyright protection. Digital watermarking provides security, authorization and copyright protection to the digital content. In digital watermarking, additional information such as text, image is embedded into the host image by making some changes in the pixel values. Watermarking technique contains the information about author, legal users of the content.

Optimal adaptive quantization is one of the key points to optimize the coding efficiency of video encoders. Latest block-based video compression standards, such as High Efficiency Video Coding (HEVC), extensively use predictive coding techniques that create dependencies between blocks and increase the complexity of optimal block quantizers search. Specifically, the motion compensation is responsible for a dependency network connecting all blocks of the same GOP together. In this paper, this dependency network is estimated by a temporal distortion propagation model and an accurate estimation of Inter and Skip modes probabilities. Optimal quantizers are then designed per block in order to achieve the global optimization in terms of Rate-Distortion efficiency. By implementing the algorithm into the HEVC reference Model (HM), we report −16.51% PSNR-based and −26.26% SSIM-based average bitrate savings compared to no adaptive quantization. The proposed algorithm outperforms several related methods from state-of-the-art. Moreover, along with the demonstration of optimal quantizer solution, we propose an in-depth analysis of the algorithm behavior. This analysis includes, among others, the relative distribution of rates between frames and the control of quantizers dynamic range.

As the popularity of digital media is growing, and world is becoming smaller, all due to the internet connectivity and WWW phenomena, the copyright protection of intellectual properties have become a necessity for prevention of illegal copying and content integrity verification. So, to achieve these requirements, a hybrid digital image watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) proposed approach in this paper. To increase and control the strength of the watermark, a scale factor value is used. Our approach, the watermark is not embedded directly on the wavelet coefficients but rather than on the elements of singular values of the cover image with modifying three level Discrete wavelet transform (DWT) HL and LH sub bands. Experimental results are provided in terms of Peak signal to noise ratio (PSNR) and Normalized cross correlation (NCC) to demonstrate the effectiveness of the proposed algorithm against various attacks.

https://www.ijert.org/a-robust-wavelet-based-fuzzy-k-means-algorithm-in-digital-image-watermarking

https://www.ijert.org/research/a-robust-wavelet-based-fuzzy-k-means-algorithm-in-digital-image-watermarking-IJERTCONV8IS01006.pdf

Protection of digital multimedia content such as audio, video, image has become an increasingly important issue for content owners and service providers. Watermarking is the best solution to achieve this copyright protection. Secret digital data can be embedded either in spatial domain or in frequency domain of the cover data. In this paper a Fuzzy K-means algorithm is used to locate the embedding location and Discrete Wavelet transform (DWT) is used for hiding watermark in low frequency band. The quality of the watermarked image and extracted watermark is tested using peak signal to noise ratio (PSNR). Robustness of proposed algorithm is tested under various attacks including salt and pepper noise, Gaussian noise, cropping and JPEG compression. Also it still gives a high quality watermarked image. The watermarking task is performed by MATLAB Program.

To reduce the detection failure of the exchanging signal power onto the OFDM subcarrier signal at uniform quantization, dynamic subcarrier mapping is applied. Moreover, to addressing low SNR's wall less than predetermine threshold, non-uniform quantization or adaptive quantization for the signal quantization size parameter is proposed. μ-law is adopted for adaptive quantization subcarrier mapping which is deployed in mobility environment, such as Doppler Effect and Rayleigh Fading propagation. In this works, sensing node received signal power then sampled into a different polarity positive and negative in μ-law quantization and divided into several segmentation levels. Each segmentation levels are divided into several sub-segment has representing one tone signal subcarrier number OFDM which has the number of quantization level and the width power. The results show that by using both methods, a significant difference is obtained around 8 dB compared to those not using the adaptive method. 1. INTRODUCTION Mobility makes CR node detects the power change at any time because every movement followed in the travel distance. According to [1-3] conducting a random movement activity in the CRN will do the unconditional uniform quantization. Wherein the level of quantization and width is the same, just powers in detection are changed to the distance. As stated in [4] quantizing the local energy using numerous symbols regional sensing determination. The method has intended for low SNR signal detection problems. An interesting proposed method has shown in [5-12], the local decision statistics in the form of log-likelihood ratio (LLR) from individual detectors are quantized and sent to the fusion centre (FC). The statistical properties of the decision statistics in the presence of quantization are established. The quantized decision statistics are sent through a channel that may cause errors. The effect of channel errors is incorporated in the analysis through Bit Error Probability (BEP). The decision of hypothesis testing from individual sensing node detectors in the form of log-likelihood ratio are quantized and sent to the master node. The result of quantized decision will be sent via channel might causes error on reception at the master node. The errors on the channel can be analysis through bit error probability (BEP). To optimize secondary transmit power allocation spectrum sharing, According to [13-18] has investigates the generalized Lloyds-type algorithm (GLA) [19-22] that utilizing quantized channel state information (CSI). A modified GLA is constructed for obtaining maximal power codebook, that proven to be generally merged and factually steady. The results describe that by using 3-4 bits of compensation, fully

Noice removal from images is an important activity for successful processing of images. The main objective of this research work is to investigate the applicability of soft computing techniques and statistical techniques for noice filtering. This paper presents a novel method for noise filtering in images using a fuzzy based statistical method. This method uses the outlierdetection technique to recognize the noise pixels. The pixel values are replaced with fuzzy logic techniquewhich depends on the properties of the selected neighbourhoodcorresponding to a particular noise pixel. Since, the algorithm only changes the pixels identified as the noise pixels, the image distortion of the proposed method is very low.The proposed method was tested with a sample of 60 images. Experimental results havebeen comparedagainst that of median filtermethodwhich is one of the frequently used image noise filtering methods. The comparisonshows that the proposed method generates images with less distortion compared to the median filter method. I. INTRODUCTION Sometimes images obtained from satellites, conventional and digital cameras lack incontrast and brightness because of the limitations of imaging sub systems and illumination conditions while capturing image. Images may have different types of noise. In image enhancement, the goal is to accentuate certain image features for subsequent analysis or for image display[1]. Examples include contrast and edge enhancement, pseudo-colouring, noise filtering, sharpening, and magnifying. Image enhancement is useful in feature extraction, image analysis and image display. The enhancement process itself does not increase the inherent information content in the data. It simply emphasizes certain specified image characteristics. Enhancement algorithms are generally interactive and application-dependent[2]. In many image processing applications, the input image has noise and thus may not show the features clearly.Image contrast enhancement techniques are of particular interest in photography, imagery, medical applications and display devices.Image enhancement can be clustered into two groups: spatial domain and frequency domain methods. In the spatial method, image pixels are directly modified to enhance the image whereas the frequency domain enhancement is conducted by modifying the frequency transform of the image[3]. However, computing the enhancement in frequency domain is a time consuming process even with fast transformation techniquesand henceit is unsuitable for real time applications[4]. Numerous contrast enhancement techniqueshave been proposed and they normalize the image intensities and often fail to produce satisfactory results for a broad range of non-uniform illumination images. Low contrast images are the images whose intensity levels of the pixels resides densely in a narrow range of the imagehistogram. The objects in this type of images are not clear or distinct. To improve the quality of the images and visual perception of human beings, different enhancement methods can be applied. Some methods work in frequency domain, some work in spatial domain and someothers work in fuzzy domain. The enhancement of noisy data, however, is a very critical process because the sharpening operation can significantly increase the noise. [5]. There are lots of classical filters in the literature to remove noise. The classical filters are the mean filters, the median filter and un-sharp masking. The mean filter or the average filter helps in smoothing operations. It suppresses the noise that smaller in size or any other small fluctuations in the image. It involves in calculating the average brightness values in some neighbourhoodí µí°¦ × í µí° §and replaces the grey level with an average value. Smoothing oraveraging operation blurs the image and does not preserve the edges[6]. These are not useful in removing noise spikes. Fuzzy filters provide promising results in imageprocessing tasks that cope withsome drawbacks of classical filters. Fuzzy filters are capable of dealing with vague and uncertaininformation.Fuzzy set theory is very useful in recovering a heavily noise corrupted image. Each pixel in the image is represented by a membership function and different types of fuzzy rules remove the noise with blurry edges[7]. A work based on features of imageenhancement and fuzzy logic has been carried out by Harish Kundra[8].This work deals with Fuzzy inference system (FIS)which helps to take the decision about the pixels of the image. Itfocuses on the removal of impulse noise with the preservation of edgesharpness and image details along with improving the contrast of the images. Also, the conventionalmean and median filters fail in this context even at low corruption levels.

In this paper, Sugeno Fuzzy inference system (FIS) has adopted depending on 2level DWT in order to ensure finding the best locations to embed the watermark data in the host image in the frequency domain of DWT and hence to increase the robustness of the proposed technique. First of all, apply DWT on the host image twice, then decompose the LH2 band into 2x2 blocks to assure the superior hiding of the bits of the watermark in a chosen places. On the other hand, FIS is performed to obtain a parameter that controls the embedding robustness. The algorithm leads to achieving robustness and imperceptibility criterion. The results for PSNR and NC with and without attacks are better for the proposed algorithm. Experimental results show the efficiency of the proposed technique. The experimental results show that the proposed technique has perfect robustness against image processing attacks.

Hybrid video coding systems use spatial and temporal predictions in order to remove redundancies within the video source signal. These predictions create coding-scheme-related dependencies, often neglected for sake of simplicity. The R-D Spatio-Temporal Adaptive Quantization (RDSTQ) solution uses such dependencies to achieve better coding efficiency. It models the temporal distortion propagation by estimating the probability of a Coding Unit (CU) to be Inter coded. Based on this probability, each CU is given a weight depending on its relative importance compared to other CUs. However, the initial approach roughly estimates the Inter probability and does not take into account the Skip mode characteristics in the propagation. It induces important Target Bitrate Deviation (TBD) compared to the reference target rate. This paper provides undeniable improvements of the original RDSTQ model in using a more accurate estimation of the Inter probability. Then a new analytical solution for local quantizers is obtained by introducing the Skip probability of a CU into the temporal distortion propagation model. The proposed solution brings −2.05% BD-BR gain in average over the RDSTQ at low rate, which corresponds to −13.54% BD-BR gain in average against no local quantization. Moreover, the TBD is reduced from 38% to 14%.

In the paper is presented a new method for efficient compression of still images, based on the Inverse Difference Pyramid (IDP) decomposition. A special version of the method is developed for the lossless compression of graphics and texts. The method is aimed at secure and flexible transfer of information via Internet. It permits the insertion of multiple digital watermarks in

The traffic of digital images has increased rapidly in the wide networks. The protection of this kind of data becomes important for many reasons such as confidentiality, authenticity and security. Nowadays, the most important engine to provide authenticity is watermarking. In this paper we present a method of invisible and robust hybrid watermarking to guarantee the authenticity and the confidentiality of images. This method or algorithm combines Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Bi-dimensional Empirical Mode Decomposition (BEMD) .In fact we propose to apply the BEMD to the original image then we take the First Intrinsic Mode Function (IMFs) decomposition of the image, we apply the Discrete Wavelet Transform (DWT) level 2, then we apply the Discrete Cosine Transform (DCT), to obtained news coefficients. It is here that we proceed with the integration of the mark coefficients after use of fuzzy set, DWT and DCT transforms. The method is not blind because it makes use of the original image for extracting the mark. The method is robust to certain usual attacks such as, filtering, adding noise and rotation. Results tested on different pictures by some attacks, are Satisfactory in terms of imperceptibility and robustness with measures of peak signal-to-noise ratio (PSNR).

A wavelet based multiresolution fuzzy clustering of digital images is described. We have applied wavelet transforms to decompose the images into different subbands in multiple resolutions, thereby eliminating noise in each low frequency subband. Then a partitive fuzzy clustering technique is proposed to be applied in multiple resolutions of the transformed image, in order to find coarser to finer clustering of the images in the image database. The relevance of the proposed multiresolution technique to clustering and retrieval of images in the compressed domain is also highlighted.

In this paper, a fully automatic scheme for hiding digital watermarks into face regions is proposed. To achieve this goal, an adaptive two-dimensional Gaussian model of skin color distribution is initially used in order to detect face regions within the initial image. Next each face region is decomposed into three levels with ten subbands, using the Discrete Wavelet Transform (DWT) and three pairs of subbands are formed (HL3, HL2), (LH3, LH2) and (HH3, HH2). Afterwards Qualified Significant Wavelet Trees (QSWTs), which are derived from the Embedded Zerotree Wavelet (EZW) algorithm and they are high-energy coefficient paths, are estimated for a pair of subbands. Finally visually recognizable watermark patterns are redundantly embedded to the coefficients of the highest energy QSWTs and the IDWT is applied to provide the watermarked face area. Performance of the proposed face region watermarking system is tested under various signal distortions such as JPEG lossy compression, sharpening and blurring. Experimental results on real life images indicate the efficiency and robustness of the proposed scheme.

In this paper, a fully automatic scheme for hiding digital watermarks into face regions is proposed. To achieve this goal, an adaptive two-dimensional Gaussian model of skin color distribution is initially used in order to detect face regions within the initial image. Next each face region is decomposed into three levels with ten subbands, using the Discrete Wavelet Transform (DWT) and three pairs of subbands are formed (HL3, HL2), (LH3, LH2) and (HH3, HH2). Afterwards Qualified Significant Wavelet Trees (QSWTs), which are derived from the Embedded Zerotree Wavelet (EZW) algorithm and they are high-energy coefficient paths, are estimated for a pair of subbands. Finally visually recognizable watermark patterns are redundantly embedded to the coefficients of the highest energy QSWTs and the IDWT is applied to provide the watermarked face area. Performance of the proposed face region watermarking system is tested under various signal distortions such as JPEG lossy compression, sharpening and blurring. Experimental results on real life images indicate the efficiency and robustness of the proposed scheme.

In this paper, a new watermarking method in discrete wavelet transform (DWT) domain is proposed. We insert the bits of a binary logo redundantly in LL subband of wavelet transform of host image to improve the watermark robustness. Watermark extraction is done using a fuzzy decision making method. In order to detect any forgery in the watermarked image we used the bits of the same logo to embed a semi-fragile watermark in detail subbands. Experimental results of this method presented PSNR values above 40db in watermarked image and acceptable robustness against attacks like JPEG compression and cropping. In addition, by implementing another fuzzy decision making method on the same watermarked image, we could detect any forgery in any location of the watermarked image.

In this paper, a highly robust watermarking scheme based on Discrete Wavelet Transform (DWT) is proposed. It has the following features. First, unlike conventional proposed watermarking techniques, a watermark is embedded into the lowest frequency components. Second, the Human Visual System (HVS) model is adopted to improve the transparency of the watermarked image. Several experiments are conducted t o demonstrate the robustness property of this watermarking scheme. Furthermore, it is worthwhile to mention that the proposed scheme is resistant to the counterfeiting attack. In addition, comparison of the proposed scheme with other state-of-the-art watermarking schemes based on DWT shows that our scheme is more effective.

In this paper, we propose a new rate control scheme designed for the newest high efficiency video coding (HEVC) standard, and aimed at enhancing the quality of regions of interest (ROI). Our approach allocates a higher bit rate to the region of interest while keeping the global bit rate close to the assigned target value. This algorithm is developed for a videoconferencing system, where the ROIs (typically, faces) are automatically detected and each coding unit is classified in a region of the interest map. This map is given as input to the rate control algorithm and the bit allocation is made accordingly. Experimental results show that the proposed scheme achieves accurate target bit rates and provides an improvement in the region of interest quality, both in objective metrics and based on subjective quality evaluation.

In this paper, a fully automatic scheme for hiding digital watermarks into face regions is proposed. To achieve this goal, an adaptive two-dimensional Gaussian model of skin color distribution is initially used in order to detect face regions within the initial image. Next each face region is decomposed into three levels with ten subbands, using the Discrete Wavelet Transform (DWT) and three pairs of subbands are formed (HL3, HL2), (LH3, LH2) and (HH3, HH2). Afterwards Qualified Significant Wavelet Trees (QSWTs), which are derived from the Embedded Zerotree Wavelet (EZW) algorithm and they are high-energy coefficient paths, are estimated for a pair of subbands. Finally visually recognizable watermark patterns are redundantly embedded to the coefficients of the highest energy QSWTs and the IDWT is applied to provide the watermarked face area. Performance of the proposed face region watermarking system is tested under various signal distortions such as JPEG lossy compression, sharpening and blurring. Experimental results on real life images indicate the efficiency and robustness of the proposed scheme.

In implementing these ways, we have a tendency to had to find out and make the processes to feature a watermark and extract a watermark from digital images . To guage the degree to that watermarking affects the first image; the user interface

Noise removal from images is an important activity for successful processing of images. The main objective
of this research work is to investigate the applicability of soft computing techniques and statistical techniques for
noise filtering. This paper presents a novel method for noise filtering in images using a fuzzy based statistical method.
This method uses the outlier detection technique to recognize the noise pixels. The pixel values are replaced with fuzzy
logic technique which depends on the properties of the selected neighborhood corresponding to a particular noise
pixel. Since, the algorithm only changes the pixels identified as the noise pixels, the image distortion of the proposed
method is very low.The proposed method was tested with a sample of 60 images. Experimental results have been
compared against that of median filter method which is one of the frequently used image noise filtering methods. The
comparison shows that the proposed method generates images with less distortion compared to the median filter
method.

Sometimes images obtained from satellites, conventional and digital cameras lack incontrast and brightness because of the limitations of imaging sub systems and illumination conditions while capturing image. Images may have different types of noise. In image enhancement, the goal is to accentuate certain image features for subsequent analysis or for image display . Examples include contrast and edge enhancement, pseudo-colouring, noise filtering, sharpening, and magnifying. Image enhancement is useful in feature extraction, image analysis and image display. The enhancement process itself does not increase the inherent information content in the data. It simply emphasizes certain specified image characteristics. Enhancement algorithms are generally interactive and application-dependent[2]. In many image processing applications, the input image has noise and thus may not show the features clearly.Image contrast enhancement techniques are of particular interest in photography, imagery, medical applications and display devices.Image enhancement can be clustered into two groups: spatial domain and frequency domain methods. In the spatial method, image pixels are directly modified to enhance the image whereas the frequency domain enhancement is conducted by modifying the frequency transform of the image[3]. However, computing the enhancement in frequency domain is a time consuming process even with fast transformation techniquesand henceit is unsuitable for real time applications . Numerous contrast enhancement techniqueshave been proposed and they normalize the image intensities and often fail to produce satisfactory results for a broad range of non-uniform illumination images. Low contrast images are the images whose intensity levels of the pixels resides densely in a narrow range of the imagehistogram. The objects in this type of images are not clear or distinct. To improve the quality of the images and visual perception of human beings, different enhancement methods can be applied. Some methods work in frequency domain, some work in spatial domain and someothers work in fuzzy domain. The enhancement of noisy data, however, is a very critical process because the sharpening operation can significantly increase the noise. . There are lots of classical filters in the literature to remove noise. The classical filters are the mean filters, the median filter and un-sharp masking. The mean filter or the average filter helps in smoothing operations. It suppresses the noise that smaller in size or any other small fluctuations in the image. It involves in calculating the average brightness values in some neighbourhood × and replaces the grey level with an average value. Smoothing oraveraging operation blurs the image and does not preserve the edges[6]. These are not useful in removing noise spikes. Fuzzy filters provide promising results in imageprocessing tasks that cope withsome drawbacks of classical filters. Fuzzy filters are capable of dealing with vague and uncertaininformation.Fuzzy set theory is very useful in recovering a heavily noise corrupted image. Each pixel in the image is represented by a membership function and different types of fuzzy rules remove the noise with blurry edges . A work based on features of imageenhancement and fuzzy logic has been carried out by Harish Kundra[8].This work deals with Fuzzy inference system (FIS)which helps to take the decision about the pixels of the image. Itfocuses on the removal of impulse noise with the preservation of edgesharpness and image details along with improving the contrast of the images. Also, the conventionalmean and median filters fail in this context even at low corruption levels.