Image and Video Compression

We present a Bayesian model that allows to automatically generate fixations/foveations and that can be suitably exploited for compression purposes. The twofold aim of this work is to investigate how the exploitation of high-level perceptual cues provided by human faces occurring in the video can enhance the compression process without reducing the perceived quality of the video and to validate such assumption with an extensive and principled experimental protocol.

In recent years the satellite communication has been growing for the development of rapid and efficient techniques for the storage and transmission of satellite images. The science of reducing the number of bits required to represent the image is called image com- pression. The aim of this paper is to reduce the size of the image, while transmitting the original image. This reduced image is called as compressed image. The compressed image is being transmitted and it is reconstructed in the receiving side so that the compressed image is being decompressed to obtain the original image. And also transmission of such satellite image increases communication accuracy but requires less bandwidth. In most systems, the amount of intimation that the user wishes to communicate or store necessities some form of compression for efficient and reliable use of communication or storage system. Although several well-known compression techniques exist, many of them require computationally intensive algorithm. In addition to that many compression techniques introduce unwanted attribute such as loss of intimation. This is a major problem in satellite imaging where image degradation may be critical. By considering the above, this paper is to use the wavelet to improve the compression ratio as well as visual quality which is implemented by the famous algorithm called sub band coding and decoding algorithm in MATLAB 7.1 software tool.

In recent trends, digital information to the industrial integration for the intelligent transportation system (ITS) field is gaining importance for the researcher, academia, and industrial persons. Visual information helps to manage traffic systems in the industrial forum to build smart cities in developed countries. This paper presents vision-based real-time vehicle detection and Vehicle Speed Measurement (VSM) using morphology operation and binary logical process for an unplanned traffic scenario using image processing techniques. Vehicle detection and VSM help to reduce the number of accidents and improve road network efficiency. The bounding box size for vehicle detection is flexible according to vehicles' sizes on the road. We test this system with different colors and dimensions for a selected Region of Interest (ROI). The ROI sets using the two-line approach. Here, we compare the proposed system with the inter-frame difference method and the blob analysis method with recall, precision, and F1 performance parameters.

Standards play an important role in providing a common set of specifications and allowing interoperability between devices and systems. Until recently, no standard for High Dynamic Range (HDR) image coding had been adopted by the market, and HDR imaging relies on proprietary and vendor specific formats which are unsuitable for storage or exchange of such images. To resolve this situation, the JPEG Committee is developing a new coding standard called JPEG XT that is backwards compatible to the popular JPEG compression, allowing it to be implemented using standard 8-bit JPEG coding hardware or software. In this paper, we present design principles and technical details of JPEG XT. It is based on a two-layers design, a base layer containing a Low Dynamic Range (LDR) image accessible to legacy implementations, and an extension layer providing the full dynamic range. The paper introduces three of currently defined profiles in JPEG XT, each constraining the common decoder architecture to a subset of allowable configurations. We assess the coding efficiency of each profile extensively through subjective assessments, using 24 naïve subjects to evaluate 20 images, and objective evaluations, using 106 images with five different tone-mapping operators and at 100 different bit rates. The objective results (based on benchmarking with subjective scores) demonstrate that JPEG XT can encode HDR images at bit rates varying from 1.1 to 1.9 bit/pixel for estimated mean opinion score (MOS) values above

ClassX is an interactive online lecture viewing system developed at Stanford University. Unlike existing solutions that restrict the user to watch only a pre-defined view, ClassX allows interactive pan/tilt/zoom while watching the video. The interactive video streaming paradigm avoids sending the entire field-of-view in the recorded high resolution, thus reducing the required data rate. To alleviate the navigation burden on the part of the online viewer, ClassX offers automatic tracking of the lecturer. ClassX also employs slide recognition technology, which allows automatic synchronization of digital presentation slides with those appearing in the lecture video. This paper presents a design overview of the ClassX system and the evaluation results of a 3-month pilot deployment at Stanford University. The results demonstrate that our system is a low-cost, efficient and pragmatic solution to interactive online lecture viewing.

Recent  advances  in  high  dynamic  range  (HDR)
capturing  and  display  technologies  attracted  a  lot  of  interest  to HDR imaging. Many issues that are considered as being resolved for  conventional  low  dynamic  range  (LDR)  images  pose  new challenges in HDR context. One such issue is a lack of standards for  HDR  image  compression.  Another  is  the  limited  availability of  suitable  image  datasets  that  are  suitable  for  studying  and evaluation of HDR image compression. In this paper, we address this problem by creating a publicly available dataset of 20 HDR images and corresponding versions compressed at four different
bit rates with three profiles of the upcoming JPEG XT standard
for HDR image compression. The images cover different scenes,
dynamic  ranges,  and  acquisition  methods  (fusion  from  several
exposures, frame of an HDR video, and CGI generated images).
The  dataset  also  includes  Mean  Opinion  Scores  (MOS)  for
each  compressed  version  of  the  images  obtained  from  extensive subjective experiments using SIM2 HDR monitor.

In this paper, we develop a batch fuzzy learning vector quantization algorithm that attempts to solve certain problems related to the implementation of fuzzy clustering in image compression. The algorithm's structure encompasses two basic components. First, a modified objective function of the fuzzy c-means method is reformulated and then is minimized by means of an iterative gradient-descent procedure. Second, the overall training procedure is equipped with a systematic strategy for the transition from fuzzy mode, where each training vector is assigned to more than one codebook vectors, to crisp mode, where each training vector is assigned to only one codebook vector. The algorithm is fast and easy to implement. Finally, the simulation results show that the method is efficient and appears to be insensitive to the selection of the fuzziness parameter.

In this report we analyse the image reconstruction accuracy when using different orthogonal basis functions as the kernel for a reversible image transform. In particular we examine the Discrete Cosine Transform(DCT), Discrete Tchebichef Transform(DTT), Haar Transform, and Walsh-Hadamard Transforms(WHT). We have found that the DCT provides the greatest energy compactness properties for continuous tone images(such as photographs). For images demonstrating rapid gradient variations the Haar Transform performs significantly better than any of the other transform we have analysed, although its performance on continuous tone images is substantially worse than either the DCT or DTT. The WHT performs poorly on either image type.

H.264 / AVC expansion is H.264 / SVC which is applicable in environments that demand video streaming. This paper delivers an algorithm to shorten computational complexity and extend coding efficiency by determining the mode speedily. In this writing, the authors talk a fast mode resolution algorithm with less complexity unlikely the traditional joint scalable video model (JSVM). Their algorithm end mode hunt by a probability model defined. This model is address for both intra-mode and inter-mode predictions of base layer and enhancement layers in a macro block (MB). The estimated rate distortion cost (RDC) for modes among layers is custom to determine the best mode of each MB. The experimental results show that the authors' algorithm realizes 26.9% of encoding time when compared with the JSVM reference software with smallest reduction in peak signal to noise ratio (PSNR).

This paper deals with primary latest video coding standard H.265/SHVC, a scalable extension to High Efficiency Video Coding (HEVC). HEVC introduces new coding tools compared to its predecessor and is backward compatible to all type of electronic gadgets. The gadgets with different display capabilities cannot be offered the same quality video due to the constraints in transmission bandwidth is a major problem. One solution to this problem will be compression of video sequence which is focused in this paper to preserve or increase PSNR while reducing bit-rate besides a novel method implemented in SHVC encoder. The novel method undergoes a combined AEGBM3D (adaptive edge guided block matching and 3D) filtering and back-propagation technique. The technique includes an AEGBM3D filter which avoids spatial redundancy and de-noise frames; hence enhancement in PSNR is achieved. The obtained PSNR of the video is compared with the set threshold PSNR so as to maintain PSNR above the threshold by repeated AEGBM3D filtering. The BP technique based on neural network machine learning approach continually restrains the output if the input block does not contain a feature they were trained to recognize. This frequent control over the output produces few bits; hence reduction in bit-rate is achieved. The simulation results show that the proposed technique delivers an average increment of 0.16 and 0.25 dB in PSNR and an average decrement of 28 and 37% in bit-rate for ×1.5 and ×2 spatial ratios, respectively, compared with the existing methods.

This paper presents a novel scheme for light field compression
based on a randomize hierarchical multi-view extension
of high efficiency video coding (dubbed as RH-MVHEVC).
Specifically, a light field data are arranged as a multiple pseudotemporal
video sequences which are efficiently compressed
withMV-HEVC encoder, following an integrated random coding
technique and hierarchical prediction scheme. The critical
advantage of proposed RH-MVHEVC scheme is that it
utilizes not just a temporal and inter-view prediction, but efficiently
exploits the strong intrinsic similarities within each
sub-aperture image and among neighboring sub-aperture images
in both horizontal and vertical directions. Experimental
results consistently outperform the state-of-the-art compression
methods on benchmark ICME 2016 and ICIP 2017 grand
challenge data sets. It achieves an average up to 33.803% BDrate
reduction and 1.7978 dB BD-PSNR improvement compared
with an advanced JEM video encoder, and an average
20.4156% BD-rate reduction and 2.0644 dB BD-PSNR improvement
compared with a latest image-based JEM-anchor
coding scheme.

The increased interest in High Dynamic Range (HDR) video over existing Low Dynamic Range (LDR) video during the last decade or so was primarily due to its inherent capability to capture, store and display the full range of real-world lighting visible to the human eye with increased precision. This has led to an inherent assumption that HDR video would be preferable by the end-user over LDR video due to the more immersive and realistic visual experience provided by HDR. This assumption has led to a considerable body of research into efficient capture, processing, storage and display of HDR video. Although, this is beneficial for scientific research and industrial purposes, very little research has been conducted in order to test the verac-ity of this assumption. In this paper, we conduct two subjective studies by means of a ranking and a rating based experiment where 60 participants in total, 30 in each experiment, were tasked to rank and rate several reference HDR video scenes along with three mapped LDR versions of each scene on an HDR display, in order of their viewing preference. Results suggest that given the option, end-users prefer the HDR representation of the scene over its LDR counterpart. Keywords HDR Video · Subjective Experiment · Ranking · Rating

Video verification on mobile devices, such as cell phones and tablet PCs, allows users to remotely check what is happening in a particular place without the need to constantly monitor the site and record video from the camera. This requires appropriate stream encoding to be applied. Unfortunately, existing encoding standards, widely used in video monitoring, have several drawbacks. In the paper, we show some remarks on choosing video stream encoding for video verification on Android-based mobile devices. Results of performed tests on bandwidth usage have shown that encoding based on passing static frames in JPEG format translates very well into an environment where the camera’s resolution is often higher than the mobile device screen.

—Medical imaging and compression are very crucial areas in terms of complexity in Image acquisition and processing. Lossless or near lossless compression is desirable for telemedicine or for storing a large amount of data; as a better acquisition (varying from 1 Tesla to 7 Tesla) larger the data volume. In this work HEVC/H.265 encoding is used to compress Computer Tomography (CT) Images. Quality evaluation is done on the basis of an objective analysis with parameter SSIM, MSE, PSNR etc. This work also reported the scheme of HEVC and its results with INTRA prediction and CABAC coding.

Linear subspace representations of appearance variation are pervasive in computer vision. This paper addresses the problem of robustly matching such subspaces (computing the similarity between them) when they are used to describe the scope of variations within sets of images of different (possibly greatly so) scales. A naive solution of projecting the low-scale subspace into the high-scale image space is described first and subsequently shown to be inadequate, especially at large scale discrepancies. A successful approach is proposed instead. It consists of (i) an interpolated projection of the low-scale subspace into the high-scale space, which is followed by (ii) a rotation of this initial estimate within the bounds of the imposed "downsampling constraint". The optimal rotation is found in the closed-form which best aligns the high-scale reconstruction of the low-scale subspace with the reference it is compared to. The method is evaluated on the problem of matching sets of (i) face appearances under varying illumination and (ii) object appearances under varying viewpoint, using two large data sets. In comparison to the naive matching, the proposed algorithm is shown to greatly increase the separation of between-class and within-class similarities, as well as produce far more meaningful modes of common appearance on which the match score is based.

In this paper, a new fractal image compression algorithm is proposed, in which the
time of the encoding process is considerably reduced. The algorithm exploits a domain pool
reduction approach, along with the use of innovative predefined values for contrast scaling
factor, S, instead of searching it across. Only the domain blocks with entropy greater than a
threshold are considered to belong to the domain pool. The algorithm has been tested for some
well-known images and the results have been compared with the state-of-the-art algorithms. The
experiments show that our proposed algorithm has considerably lower encoding time than the
other algorithms giving approximately the same quality for the encoded images.

Image colorization is a new image processing topic to recolor gray images to look as like the original color images as possible. Different methods have appeared in the literature to solve this problem, the way that leads to thinking about decolorization, eliminating the colors of color images to just small color keys, aid in the colorization process. Due to this idea, decolorization is considered as a color image encoding mechanism. In this chapter, the authors propose a new decolorization system depends on extracting the color seeds (Representative Pixels [RP]) using morphology operations. Different decolorization methods are studied and compared to the system results using different quality metrics.

This paper proposes a unidirectional encoder rate control (ERC) scheme in the interpolation based distributed video coding. As the encoder is complexity constrained, accurate estimation of number of bits to decode each bit plane is indeed difficult at the encoder. In case of under-estimation of the bits, correction of the errors in the decoded bit planes, by utilizing the available information, is one of the important tasks at the decoder. This was addressed by recent schemes. In this paper, we present an improved ERC, considering higher group of pictures(GOP). The contributions of the proposed scheme are (1) adaptive rate estimation, considering the dependency across Wyner-Ziv frames (2) motion adaptive reconstruction and (3) Side information refinement after decoding all the frames in the GOP. The proposed scheme is tested with several sequences, showing improvements in the case of GOP-4.

In this paper, two simple principal component regression methods for estimating the optical flow between frames of video sequences according to a pel-recursive manner are introduced. These are easy alternatives to dealing with mixtures of motion vectors in addition to the lack of prior information on spatial-temporal statistics (although they are supposed to be normal in a local sense). The 2D motion vector estimation approaches take into consideration simple image properties and are used to harmonize regularized least square estimates. Their main advantage is that no knowledge of the noise distribution is necessary, although there is a underlying assumption of localized smoothness. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.

We present a new preprocessing technique for two-dimensional compression of surface electromyographic (S-EMG) signals, based on correlation sorting. We show that the JPEG2000 coding system (originally designed for compression of still images) and the H.264/AVC encoder (video compression algorithm operating in intraframe mode) can be used for compression of S-EMG signals. We compare the performance of these two off-the-shelf image compression algorithms for S-EMG compression, with and without the proposed preprocessing step. Compression of both isotonic and isometric contraction S-EMG signals is evaluated. The proposed methods were compared with other S-EMG compression algorithms from the literature.

Most wavelet-based scalable image compression algorithms are optimized for performance only, i.e., they try to produce a compressed bit-stream that gives the best image quality for the selected bit-rate. Unfortunately, the optimization of the coding method may result in a bit-stream that cannot be rearranged easily to support resolution scalability. This paper proposes two algorithms. The first algorithm produces a rate scalable bit-stream that is optimized for performance and scalability. That is, it has good performance and at the same time it produces bit-stream that can be easily rearranged to become highly (rate and resolution) scalable. To this end, the algorithm combines the low complexity and rate scalability features of the set partitioning coding approach, and it exploits the resolution scalability characteristics of the block-based systems. The second algorithm upgrades the first algorithm to produce a highly scalable bit-stream by only arranging and identifying the different resolutions within the bit-stream.

Nowadays the growing availability of stereo cameras for common applications is