Entropy Encoder

This paper presents a design of source encoding calculator software which applies the two famous algorithms in the field of information theory- the Shannon-Fano and the Huffman schemes. This design helps to easily realize the algorithms without going into a cumbersome, tedious and prone to error manual mechanism of encoding the signals during the transmission. The work describes the design of the software, how it works, comparison with related works, its efficiency, its usefulness in the field of information technology studies and the future prospects of the software to engineers, students, technicians and alike. The designed "Encodia" software has been developed, tested and found to meet the intended requirements. It is expected that this application will help students and teaching staff in their daily doing of information theory related tasks. The process is ongoing to modify this tool so that it can also be more intensely useful in research activities on source coding.

Image compression can be described as a way of minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. This paper presents the study of image compression using Discrete Cosine Transform (DCT). This work employs DCT method to compress images in other to have high compression ratio and high quality image. However, this study presents a number of merits, namely: reduction in redundancies of the image data so as to transmit data in an efficient form and ability to attach and transfer images over the web or over the network in less time. (

� Abstract— The increase popularity of multimedia application especially in image processing places a great demand on efficient data storage and transmission techniques. Network communication such as wireless network can easily be intercepted and cause of confidential information leaked. Unfortunately, conventional compression and encryption methods are too slow; it is impossible to carry out real time secure image processing. In

Lossy JPEG compression is a widely used compression technique. Normally the JPEG technique uses two process quantization, which is lossy process and entropy encoding, which is considered lossless process. In this paper, a new technique has been proposed by combining the JPEG algorithm and Symbol Reduction Huffman technique for achieving more compression ratio. The symbols reduction technique reduces the number of symbols by combining together to form a new symbol. As a result of this technique the number of Huffman code to be generated also reduced. The result shows that the performance of standard JPEG method can be improved by proposed method. This hybrid approach achieves about 20% more compression ratio than the Standard JPEG.

The JPEG standard technique involves three process mapping reduces interpixel redundancy, quantization, which is lossy process and entropy encoding, which is considered lossless process. Lossy JPEG compression is commonly used image compression technique. In the present paper a new hybrid technique has been introduced by combining the JPEG algorithm and Symbol Reduction Huffman technique for achieving more compression ratio .In the symbols reduction method , the number of symbols are reduced by combining together to form a new symbol. As a result of this method the number of Huffman code to be generated also reduced. It is simple, fast and easy to implement. The result shows that the performance of standard JPEG technique can be improved by proposed method. This new hybrid approach achieves about 20% more compression ratio than the Standard JPEG.

The paper attempts a comparison between Huffmancoding and Position Based Coding Scheme introduced by the authors.After a review of various image compression standards and image compression coders, it is observed that there is a need to study the post-transformation matrix in a JPEGenvironment and accordingly, brought out a coding scheme based on the position of elements of the transform coefficients matrix after performing quantization. By identifying the unique elements and by reducing redundancies, the paper presented a novel method of coding called, PBCS. Thereafter, the results of Joint Picture Expert Group (JPEG) with Huffman coding and PBCS are compared. The results showbetter compression ratio with higher PSNR and better image quality with quantization. The study can be considered as a logical extension of the discrete cosine transformation matrix of an image,andapplies statistical tools to achieve the novel coding scheme.The coding scheme can highly economisethe bandwidth wi...

The paper attempts a comparison between Huffmancoding and Position Based Coding Scheme introduced by the authors.After a review of various image compression standards and image compression coders, it is observed that there is a need to study the post-transformation matrix in a JPEGenvironment and accordingly, brought out a coding scheme based on the position of elements of the transform coefficients matrix after performing quantization. By identifying the unique elements and by reducing redundancies, the paper presented a novel method of coding called, PBCS. Thereafter, the results of Joint Picture Expert Group (JPEG) with Huffman coding and PBCS are compared. The results showbetter compression ratio with higher PSNR and better image quality with quantization. The study can be considered as a logical extension of the discrete cosine transformation matrix of an image,andapplies statistical tools to achieve the novel coding scheme.The coding scheme can highly economisethe bandwidth wi...

The JPEG standard technique involves three process mapping reduces interpixel redundancy, quantization, which is lossy process and entropy encoding, which is considered lossless process. Lossy JPEG compression is commonly used image compression technique. In the present paper a new hybrid technique has been introduced by combining the JPEG algorithm and Symbol Reduction Huffman technique for achieving more compression ratio .In the symbols reduction method , the number of symbols are reduced by combining together to form a new symbol. As a result of this method the number of Huffman code to be generated also reduced. It is simple, fast and easy to implement. The result shows that the performance of standard JPEG technique can be improved by proposed method. This new hybrid approach achieves about 20% more compression ratio than the Standard JPEG.

In today's communicative and multimedia computing world, JPEG images play a vast consequential role. The JPEG images have been able to satisfy the users by fulfilling their demand of preserving numerous digital images within considerably less storage space. Although the JPEG standard offers four different sorts of compression mechanism, among them the Baseline JPEG or Lossy Sequential DCT Mode of JPEG is most popular since it can store a digital image by temporarily removing its psychovisual redundancy and thereby offering a very less storage space for a large image. Again, the computational complexity of Baseline JPEG is also considerably less as compression takes place in Discrete Cosine Transform domain. Therefore, Baseline JPEG is substantially useful while storing, sharing and transmitting digital images. Despite removing a large amount of psychovisual redundancy, the Baseline JPEG still contains redundant data in DCT domain. This paper explores the fact and introduces an improved technique that modifies the Baseline JPEG algorithm. It describes a way to further compress a JPEG image without any additional loss while achieving a better compression ratio than that is achievable by Baseline JPEG. The contribution of this work is to incorporate a simple mathematical series with Baseline JPEG before applying optimal encoding and perform a Selective Quantization that essentially does not loss any information after decompression but reduces the redundant data in DCT domain. The proposed technique is tested on over 200 textbook images that are extensively used for testing standard Image Processing and Computer Vision algorithms. The experimental results show that our proposed approach achieves 2.15% and 14.10% better compression ratio than that is achieved by Baseline JPEG on an average for gray-scale and true-color images respectively.

In today's communicative and multimedia computing world, JPEG images play a vast consequential role. The JPEG images have been able to satisfy the users by fulfilling their demand of preserving numerous digital images within considerably less storage space. Although the JPEG standard offers four different sorts of compression mechanism, among them the Baseline JPEG or Lossy Sequential DCT Mode of JPEG is most popular since it can store a digital image by temporarily removing its psychovisual redundancy and thereby offering a very less storage space for a large image. Again, the computational complexity of Baseline JPEG is also considerably less as compression takes place in Discrete Cosine Transform domain. Therefore, Baseline JPEG is substantially useful while storing, sharing and transmitting digital images. Despite removing a large amount of psychovisual redundancy, the Baseline JPEG still contains redundant data in DCT domain. This paper explores the fact and introduces an improved technique that modifies the Baseline JPEG algorithm. It describes a way to further compress a JPEG image without any additional loss while achieving a better compression ratio than that is achievable by Baseline JPEG. The contribution of this work is to incorporate a simple mathematical series with Baseline JPEG before applying optimal encoding and perform a Selective Quantization that essentially does not loss any information after decompression but reduces the redundant data in DCT domain. The proposed technique is tested on over 200 textbook images that are extensively used for testing standard Image Processing and Computer Vision algorithms. The experimental results show that our proposed approach achieves 2.15% and 14.10% better compression ratio than that is achieved by Baseline JPEG on an average for gray-scale and true-color images respectively.

In recent times the integration of video, audio and data in telecommunication devices has revolutionized communication world. It has proven to be useful to almost every industry: the corporate world, entertainment industry, multimedia, education and even at home. The major problems associated with these applications are the high data rates, high bandwidth and large memory required for storage and computing resources. Even with faster internet speed, throughput rates and advanced network infrastructure, there are major bottlenecks to transfer such high volume data through the network due to bandwidth limitations. There is a need to develop compression techniques in order to make the best use of available bandwidth. Thus storage and compression of these high resolution images plays a vital role in such applications to conserve the energy and processor's computational resources. This paper presents a lightweight modified TiBS algorithm for image compression and storage. The proposed modified compression method operates on a 3x3 block and is based on pixel removal technique. The results shows that proposed method provides a maximum compression of 33% which is more than that achievable by standard TiBS algorithm.

Today more and more digital medical images are used by physicians in their clinical diagnosis. DICOM format images are used by physicians in their clinical diagnosis. DICOM images are too large in size. They require much space for storage and bandwidth for transmission. Thus, medical images are to be compressed due to their large size and repeated usage for diagnostic purpose. Three pattern Huffman compression algorithm uses the concept of pattern creation. The operation of pattern creation is to create patterns using fixed length coding. Using these patterns, compression of image is carried out. Three best patterns are created in such a way, which yields better compression ratios. Three pattern Huffman compression algorithm uses lossless compression technique and can be applied to all types of medical images like CT scans, MRIs, PET, etc. without compromise in quality. The core concept of the algorithm is based on building up a collection of n-length patterns in the image. The basic model of new compression algorithm is similar to that of the Huffman encoder except for the pattern finder. The operation of the pattern finder is to find the best pattern, which is the most frequent occurring pattern. Therefore the best pattern will also be an input to the encoder. The output of the encoder will be the code along with footer information.

Image compression is a technique to reduce the volume of information to be transmitted. Compression technique reduces the bandwidth and the volume of the information to be transmitted. We can compress audio signal, video signal, text, fax and images. For medical images lossless compression is used and for other types lossy compression can be used. For compressing an image we can use the DCT technique. But after compression during decompression and recovering original image from compressed image we can face problem of blocking artifacts. Various methods can be used for removing blocking artifacts. One of them is DCT filtering. Further for getting better results we can remove blocking artifacts by spatial and hybrid filtering method. Our experimental results shows that hybrid filtering gives better performance on the bases of better PSNR, BER and MSE.

Image compression is a technique to reduce the volume of information to be transmitted. Compression technique reduces the bandwidth and the volume of the information to be transmitted. We can compress audio signal, video signal, text, fax and images. For medical images lossless compression is used and for other types lossy compression can be used. For compressing an image we can use the DCT technique. But after compression during decompression and recovering original image from compressed image we can face problem of blocking artifacts. Various methods can be used for removing blocking artifacts. One of them is DCT filtering. Further for getting better results we can remove blocking artifacts by spatial and hybrid filtering method. Our experimental results shows that hybrid filtering gives better performance on the bases of better PSNR, BER and MSE.

Compression refers to reducing the quantity of data, bits used to represent, store and/or transmit file content, without excessively reducing the quality of the original data. Data Compression technique can be Lossless which enables exact reproduction of the original data on decomposition or Lossy which does not. The research focused on frequency of occurrence of words rather than characters, C# was the implementation language.  Using RAD approach, the algorithms was implemented and evaluated along compression ratio. The savings percentage for text data is approximately 90%. It was deduced that the performance of data compression depend on the characteristics of the files, the different symbols and symbol frequencies.

Data compression is the reduction or elimination of redundancy in data representation in order to achieve savings in storage and communication costs. Data compression techniques can be broadly classified into two categories: Lossless, Lossy schemes. Digital images require an enormous amount of space for storage. The architecture exploits the principles of pipelining and parallelism to the maximum extent in order to obtain high speed the architecture for discrete cosine transforms and the entropy encoder are based on efficient algorithms designed for high speed VLSI. For example, a colour image with a resolution of 1024 x 1024 picture elements (pixels) with 24 bits per pixel would require 3.15 Mbytes in uncompressed form. Very high-speed design of efficient compression techniques will significantly help in meeting that challenge. The JPEG baseline algorithm consists mainly of two parts: (i) Discrete Cosine Transform (DCT) computation and (ii) Entropy encoding. The architecture for entropy encoding is based on a hardware algorithm designed to yield maximum clock speed

The JPEG standard technique involves three process mapping reduces interpixel redundancy, quantization, which is lossy process and entropy encoding, which is considered lossless process. Lossy JPEG compression is commonly used image compression technique. In the present paper a new hybrid technique has been introduced by combining the JPEG algorithm and Symbol Reduction Huffman technique for achieving more compression ratio .In the symbols reduction method , the number of symbols are reduced by combining together to form a new symbol. As a result of this method the number of Huffman code to be generated also reduced. It is simple, fast and easy to implement. The result shows that the performance of standard JPEG technique can be improved by proposed method. This new hybrid approach achieves about 20% more compression ratio than the Standard JPEG.

Usage of Image has been increasing and used in many applications. The need for an efficient technique for
compression of images ever increasing because the raw images need large amounts of disk space seems to be a big
disadvantage during storage & transmission. Image compression plays on important role in saving storage space and saving
time while sending images over the network without excessively reducing the quality of the picture. There are many
compression techniques, which are presented for better use of storage devices. A compression technique explained to
achieve more compression ratio by reducing the number of source symbols. In this paper, Lossless method of image
compression using a simple coding technique called Huffman’s coding is explained.

The paper attempts a comparison between Huffmancoding and Position Based Coding Scheme introduced by the authors.After a review of various image compression standards and image compression coders, it is observed that there is a need to study the post-transformation matrix in a JPEGenvironment and accordingly, brought out a coding scheme based on the position of elements of the transform coefficients matrix after performing quantization. By identifying the unique elements and by reducing redundancies, the paper presented a novel method of coding called, PBCS. Thereafter, the results of Joint Picture Expert Group (JPEG) with Huffman coding and PBCS are compared. The results showbetter compression ratio with higher PSNR and better image quality with quantization. The study can be considered as a logical extension of the discrete cosine transformation matrix of an image,andapplies statistical tools to achieve the novel coding scheme.The coding scheme can highly economisethe bandwidth without compromising on picture quality; invariant to the existing compression standards and lossy as well as lossless compressions which offers possibility for wide ranging applications.