New Image Compression/Decompression Technique Using Arithmetic Coding Algorithm

We present a practical algorithm for the homophonic coding of a message source, as required for cryptographic purposes. The purpose of homophonic coding is to transform the output of a non-uniformly distributed message source into a random, uniformly distributed source sequence. This is achieved by mapping each source symbol into one of a set of homophones in a random way. The selected homophones are then encoded by means of arithmetic coding. In this paper a new arithmetic coding algorithm is introduced, which relies on the fact that the homophones are chosen be dyadic. This leads to particularly simple implementation of homophonic coding, requiring no multiplications but only shifts and additions. The operation of the algorithm is demonstrated by the homophonic encoding of an ASCII file. The results show that homophonic does increase the entropy by less than 2 bits per symbol, and also provides source encoding (data compression) of the original file.

The transmission of image data acquired by remote sensing missions, based on space borne platforms is a major bottleneck, as a result of the limitation of on-board power, the huge volume of transmitted data, and the number of accessible ground receiving stations. In particular these arguments hold true for small satellites that faces additional design constraints in terms of size, mass and cost .To overcome downlink restriction, image compression has to be applied, although the provided data transmission rates are constantly growing, they can't keep up with the exponentially increasing data flood provided by the scanners, the reasons are twofold: First the acquisition rate exceeds the transmission rate. Second the satellite is not in constant visibility of ground receiving station, which effectively limits the downlinkable data volume per orbit. This paper introduces implementation of some of the compression techniques for both lossy and lossless which are used for the On-Board satellite missions, taking into consideration that the introduced compression method should support and enable the real time imaging and transmission. Also, a comparison study between both techniques is introduced based on measures like Compression Ratio (CR), Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR).The experimental results showed that: Huffman code is the most suitable code for lossless compression technique for satellite images, since it gives a considerable high compression ratio with respect to the other lossless algorithms (Run-Length Encode, Lempel-Ziv-Welch code and Arithmetic code). Huffman code does not require much storage capacity such as LZW or more computational requirements such as arithmetic code. Also, the most suitable lossy compression techniques for satellite images is applying Discrete Wavelet Transform (DWT) on the four Most Significant Bit Planes (MSPB) of the image then applying the arithmetic code over the resultant DWT coefficients. This technique achieves higher average compression ratio (8.28:1) than the two other techniques and at the same time the image quality is accepted.

Lossless gray scale image compression is necessary for many purposes, such as medical imaging, image databases and so on. Lossy images are important as well, because of the high compression ratio. The authors propose a lossless image compression scheme using a lossy image generated with the JPEG-DCT scheme. The concept is, send a JPEG-compressed lossy image primarily, then send residual information and reconstruct the original image using both the lossy image and residual information. 3D adaptive prediction and adaptive arithmetic coding are used, which fully use the statistical parameters of the distribution of the symbol source. The optimal number of neighbor pixels and lossy pixels used for prediction is discussed. The compression ratio is better than previous work and quite close to the original lossless algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Summary form only given. The next-generation space telescope (NGST) will produce about 600 GB/day, assuming we use the NASA yardstick 8k/spl times/8k NIR camera (16 bits/pixel), save and transmit 64 non-destructive read-outs per image, and the camera is in continuous use (about 80 observations/day, 10/sup 3/ s each). However, with an L2 halo orbit, the NASA NGST study estimates a downlink rate of 5.35 GB/day using X-band. Clearly the volume of data to downlink must be reduced by at least a factor of 100. Astronomical images are noisy. This fact makes them difficult to compress by lossless compression algorithms such as Huffman, Lempel-Ziv, run-length, or arithmetic code. However, they also have the virtue of showing similar values among adjacent pixels. Techniques such as Rice's algorithm (Rice et al., 1993) and derivatives (White and Becker, 1998; Stiavelli and White, 1997) can take advantage of this. We present the way in which some of these compression techniques would work with NGST images. Unfortunately, these lossless algorithms give us compression ratios that still exceed the telemetry guidelines. We have also looked into the feasibility of doing lossy compression by scaling the original image prior to the lossless compression. Under this scheme, we find substantial data reduction with a negligible effect on the data quality.

Inspired by theoretical results on universal modeling, a general framework for sequential modeling of gray-scale images is proposed and applied to lossless compression. The model is based on stochastic complexity considerations and is implemented with a tree structure. It is efficiently estimated by a modification of the universal algorithm context. The sequential, lossless compression schemes obtained when the context modeler is used with an arithmetic coder, are tested with a representative set of gray-scale images. The compression ratios are compared with those obtained with state-of-the-art algorithms available in the literature, with the results of the comparison, showing the potential of the proposed approach.

In recent research we proposed GP-zip, a system which uses evolution to find optimal ways to combine standard compression algorithms for the purpose of maximally losslessly compressing files and archives. The system divides files into blocks of predefined length. It then uses a linear, fixed-length representation where each primitive indicates what compression algorithm to use for a specific data block. GP-zip worked well with heterogonous data sets, providing significant improvements in compression ratio compared to some of the best standard compression algorithms. In this paper we propose a substantial improvement, called GP-zip*, which uses a new representation and intelligent crossover and mutation operators such that blocks of different sizes can be evolved. Like GP-zip, GP-zip* finds what the best compression technique to use for each block is. The compression algorithms available in the primitive set of GP-zip* are: Arithmetic coding (AC), Lempel-Ziv-Welch (LZW), Unbounded Prediction by Partial Matching (PPMD), Run Length Encoding (RLE), and Boolean Minimization. In addition, two transformation techniques are available: the Burrows-Wheeler Transformation (BWT) and Move to Front (MTF). Results show that GP-zip* provides improvements in compression ratio ranging from a fraction to several tens of percent over its predecessor.

A binary wavelet transform (BWT) has several distinct advantages over a real wavelet transform when applied to binary data. No quantisation distortion is introduced and the transform is completely invertible. Since all the operations involved are modulo-2 arithmetic, it is extremely fast. The outstanding qualities of the BWT make it suitable for binary image-processing applications. The BWT, originally designed for binary images, is extended to the lossless compression of grey-level images. An in-place implementation structure of the BWT is explored. Then, a simple embedded lossless BWT-based image-coding algorithm called progressive partitioning binary wavelet-tree coder (PPBWC) is proposed. The proposed algorithm is simple in concept and implementation, but achieves promising lossless compression efficiency as compared with the conventional bitplane scanning methods. Small alphabets in the arithmetic coding, non-causal adaptive context modelling and source division are the major factors that contribute to the gain of compression efficiency of the PPBWC. Experimental results show that the PPBWC outperforms most of other embedded coders in terms of coding efficiency.

In embedded system design, memory is one of the most restricted resources, posing serious constraints on program size. Code compression has been used as a solution to reduce the code size for embedded systems. Lossless data compression techniques are used to compress instructions, which are then decompressed on-the-fly during execution. Previous work used fixed-to-variable coding algorithms that translate fixed-length bit sequences into variable-length bit sequences. In this paper, we present a class of code compression techniques called variable-to-fixed code compression (V2FCC), which uses variable-to-fixed coding schemes based on either Tunstall coding or arithmetic coding. Though the techniques are suitable for both reduced instruction set computer (RISC) and very long instruction word (VLIW) architectures, they favor VLIW architectures which require a high-bandwidth instruction prefetch mechanism to supply multiple operations per cycle, and fast decompression is critical to overcome the communication bottleneck between memory and CPU. Experimental results for a VLIW embedded processor TMS320C6x show that the compression ratios using memoryless V2FCC and Markov V2FCC are around 82.5% and 70%, respectively. Decompression unit designs for memoryless V2FCC and Markov V2FCC are implemented in TSMC 0.25-/spl mu/m technology.

As the coming era of digitized medical information, a close-at-hand challenge to deal with is the storage and transmission requirement of enormous data, including medical images. Compression is one of the indispensable techniques to solve this problem. In this work, we propose a dynamic vector quantization (DVQ) scheme with distortion-constrained codebook replenishment (DCCR) mechanism in wavelet domain. In the DVQ-DCCR mechanism, a novel tree-structure vector and the well-known SPIHT technique are combined to provide excellent coding performance in terms of compression ratio and peak signal-to-noise ratio for lossy compression. For the lossless compression in similar scheme, we replace traditional 9/7 wavelet filters by 5/3 filters and implement the wavelet transform in the lifting structure. Furthermore, a detection strategy is proposed to stop the SPIHT coding for less significant bit planes, where SPIHT begins to lose its coding efficiency. Experimental results show that the proposed algorithm is superior to SPIHT with the arithmetic coding in both lossy and lossless compression for all tested images.

The problem of increasing the efficiency of coding of halftone images in the space of bit planes of differences in pixel values obtained using differential coding (DPCM – Differential pulse-code modulation) is considered. For a compact representation of DPCM pixel values, it is proposed to use a combined compression encoder that implements arithmetic coding and run-length coding. An arithmetic encoder provides high compression ratios, but has high computational complexity and significant encoding overhead. This makes it effective primarily for compressing the mean-value bit-planes of DPCM pixel values. Run-length coding is extremely simple and outperforms arithmetic coding in compressing long sequences of repetitive symbols that often occur in the upper bit planes of DPCM pixel values. For DPCM bit planes of pixel values of any image, a combination of simple run length coders and complex arithmetic coders can be selected that provides the maximum compression ratio for each bit plane and all planes in general with the least computational complexity. As a result, each image has its own effective combined encoder structure, which depends on the distribution of bits in the bit planes of the DPCM pixel values. To adapt the structure of the combined encoder to the distribution of bits in the bit planes of DPCM pixel values, the article proposes to use prediction of the volume of arithmetic code based on entropy and comparison of the obtained predicted value with the volume of run length code. The entropy is calculated based on the values of the number of repetitions of ones and zero symbols, which are obtained as intermediate results of the run length encoding. This does not require additional computational costs. It was found that in comparison with the adaptation of the combined encoder structure using direct determination of the arithmetic code volume of each bit plane of DPCM pixel values, the proposed encoder structure provides a significant reduction in computational complexity while maintaining high image compression ratios.

Lossless compression schemes with secure transmission play a key role in telemedicine applications that helps in accurate diagnosis and research. Traditional cryptographic algorithms for data security are not fast enough to process vast amount of data. Hence a novel secured lossless compression approach proposed in this paper is based on reversible integer wavelet transform, EZW algorithm arithmetic coding and selective bit scrambling. The use of the lifting scheme allows to generate truly lossless integer-to-integer wavelet transforms. Images are compressed/decompressed by well-known EZW algorithm and arithmetic coding is used to improve the compression performance and also increases the security level. The proposed approach employs scrambling method, which is fast, simple to implement, and it provides security. Lossless compression ratios and distortion performance of this proposed methods are found to be better than other lossless techniques.

An antidictionary is particularly useful for data compression, and on-line electrocardiogram (ECG) lossless compression algorithms using antidictionaries have been proposed. They work in real-time with constant memory and give better compression ratios than traditional lossless data compression algorithms, while they only deal with ECG data on a binary alphabet. This paper proposes on-line ECG lossless compression for a given data on a finite alphabet. The proposed algorithm gives not only better compression ratios than those algorithms but also uses less computational space than they do. Moreover, the proposed algorithm work in real-time. Its effectiveness is demonstrated by simulation results.

4 - Arithmetic Coding

Arithmetic coding (AC) is widely used for lossless data compression, and parallelization of arithmetic coding is relatively simple because all symbols can be encoded independently. On the other hand, Parallel adaptive arithmetic coding stores the data model for each data block (the model contains the data structure used to calculate the probability distribution of the data) and these data models need to be frequently accessed and modified during encoding and decoding, so the access latency and computational complexity of the data model are important factors affecting performance. In this paper, we present a new data structure called partial prefix sum array which can quickly calculate the probability distribution of the data, so that parallel adaptive arithmetic encoding and decoding can be accelerated. Furthermore, we use a mode of coalescing access to access global memory, thereby improving the throughput of global memory. Experimental results for 6 files on NVIDIA Tesla M60 GPU show that our GPU adaptive arithmetic encoding and decoding run 1.61x- 2.75x times and 1.03x-2.22x times faster than previously presented GPU implementation, respectively.

Arithmetic coding technique is very much effective and flexible and it is evident in most of the circumstances while on the other hand AES is advanced encryption standard process which deals with changeover and permutation of data for proper security. The paper presents the security and compression of data by Arithmetic coding with AES (Advanced Encryption Standard) algorithm. Basic research is to arithmetically encode the data first, followed by encryption by using AES algorithm and then transmits the code. At the receiving end the data is decrypted and decoded to produce the user data. The paper has an advantage of encoding/decoding and compression of data both at the same time. The input data size is of 128 bits or 256 bits in AES, so the idea is to compress the data before encryption by digital arithmetic coding. The arithmetic coding and Huffman coding are very much alike in their working as they both achieve the task of compression by plummeting the average number of bits required to represent each and every symbol.