A New Method Which Combines Arithmetic Coding with RLE for Lossless Image Compression

Lossless text compression methods involve some form of moderately high-order exact string matching. However, this work cannot easily be carried over to lossless image compression, because images are two-dimensional and (more important) essentially quantized analog data. A better plan is to find and encode as much of the image structure of the data as possible, and then to encode efficiently the unstructured, noisy residual. In three steps the authors predict the value of each pixel, model the error of the prediction, and encode the error of the prediction. Having a probabilistic model for the errors, they can use arithmetic coding to encode the errors efficiently with respect to the model.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The article considers the relevance of the development of lossless image compression and transmission algorithms and their application for creating transport video surveillance systems. A brief overview of lossless transport image compression methods is provided. We propose a method for compressing transport plots based on the pyramid-recursive method of splitting the source image into polygons of various shapes and sizes. We consider two new algorithms for implementing the proposed method that are fundamentally different from each other: with a transition to the spectral region and without a transition to the spectral region of the original signal to ensure lossless compression. The results of testing various well-known lossless compression algorithms are analyzed: series length, Huffman, and arithmetic encoding, and compared with the proposed algorithms. It is shown that the proposed algorithms are more efficient in terms of compression ratio (2—3 times) compared to the known ones, while the computational complexity increases approximately by more than 3-4 times.

A color-mapped image is composed of a palette and an image of indices. The color of each pixel is the palette color referenced by the pixel's index. The indices image is usually far from being smooth. This affects its compressibility by lossless image compression methods, due to their reliance on the natural smoothness of images. The indices images can be smoothed by re-ordering the palette, ie, assigning the indices to the colors in a better way. We propose a new method for palette ordering based on an approximate solution of the traveling salesman problem. The proposed method has low complexity, dependent only on the size of the palette. It is found to significantly improve the compression ratios of color-mapped images coded with the JPEG-LS lossless compression standard. The resulting compression ratios are better than those achieved by the popular GIF image file format.

We consider and evaluate the context clustering method for lossless image compression based on the existing LOCO-I algorithm used in JPEG-LS - the latest lossless image compression standard. We employ the LOCO-I Medpredictor to enroll the error pixels. The contexts are defined by calculating gradient of current pixels. The three directional gradients are quantized with different codebook size (7, 9, 19) respectively. The error pixels are then corrected and encoded by the clustered-contexts. A main advantage of using the context clustering method is that it can eliminate the storage of probability vector. An adaptive arithmetic encoder is also introduced to yield a higher compression rate.

Pixelwise linear prediction using backward-adaptive least-squares or weighted least-squares estimation of prediction coefficients is currently among the state-of-the-art methods for lossless image compression. While current research is focused on mean intensity prediction of the pixel to be transmitted, best compression requires occurrence probability estimates for all possible intensity values. Apart from common heuristic approaches, we show how prediction error variance estimates can be derived from the (weighted) least-squares training region and how a complete probability distribution can be built based on an autoregressive image model. The analysis of image stationarity properties further allows deriving a novel formula for weight computation in weighted least-squares proofing and generalizing ad hoc equations from the literature. For sparse intensity distributions in non-natural images, a modified image model is presented. Evaluations were done in the newly developed C++ framework volumetric, artificial, and natural image lossless coder (Vanilc), which can compress a wide range of images, including 16-bit medical 3D volumes or multichannel data. A comparison with several of the best available lossless image codecs proofs that the method can achieve very competitive compression ratios. In terms of reproducible research, the source code of Vanilc has been made public.

Several lossless image compression methods are compared for their performance. The results show that using different compression techniques for different types of images will be an approach in real applications.

In this paper, a new method lossless for real time compression of still images in the field of the artificial vision, is presented. The proposed method is based on a new focus, which incorporates the simultaneous execution of the acquisition and compression process of an image. The method consists of three processes. In the first one, a linear camera captures a line, which is stored in memory. In the next step, the compression process of this line is executed at the same time as the acquisition process reads the next line. The process is repeated downwards starting from the upper line of the image. The novelty and main contribution of this method is in the compression process. A new coding algorithm is formulated and developed. The proposed method guarantees a ratio of compression of 3:1 with high probability and a real time execution.

This paper presents a new lossless image compression method based on the learning of pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and contexts obtained by MLPs are forwarded to adaptive arithmetic encoders, like the conventional lossless compression schemes. The MLP-based prediction has long been attempted for lossless compression, and recently convolutional neural networks (CNNs) are also adopted for the lossy/lossless coding. While the existing MLP-based lossless compression schemes focused only on accurate pixel prediction, we jointly predict the pixel values and contexts. We also adopt and design channel-wise progressive learning, residual learning, and duplex network in this MLP-based framework, which leads to improved coding gain compared to the conventional methods. Experiments show that the proposed method performs better than the conventional non-learning algorithms and also recent learning-based compression methods with practical computation time.

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.

A novel near lossless image compression method is proposed in this paper. In the proposed method, a redundant representation of the image is firstly obtained by pixel level shifting and then partitioned into image blocks with the same size. Next, the image blocks are classified into smooth image blocks and non-smooth image blocks. For the smooth image blocks, improved block coding which includes mean residual error is utilized for pixel rectangles with the same pixel value in each to remove the redundancy. Lastly, the whole image is scanned by efficient image scanning that only one pixel is scanned for each pixel rectangle and then compressed by Huffman coding. Simulation results indicate that the proposed method has good performance compared with traditional lossless/near lossless image compression methods.

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>

In this paper a new lossless image compression method named DPCM-IWPT is proposed. First, wavelet packets transform (WPT) and lifting scheme (LS) are described. Then an application of the LS to WPT is presented which leads to the generation of integer wavelet packets transform (IWPT). After analyzing and implementing the effect of differential pulse code modulation (DPCM) on lossless image compression, a new method combining DPCM and IWPT (DPCM-IWPT) is proposed. Comparing with IWT and IWPT, the method DPCM-IWPT shows better performance in lossless image compression.

In this paper, a method for lossless and near lossless compression of large digital mammograms is proposed. This method is based on a predictive coder that uses integer-to-integer operations, and generates a two-layer embedded bit stream (i.e. near-lossless and refinement layer). The maximum tolerated pixel distortion is guaranteed in the near lossless component. Comparisons with other proposed approaches are based on a database of high-resolution 12 bits/pixel digital mammograms. The simulation results indicate that our lossless compression method can offer average bit rates 39, 5%, 24%, 7% and 2.5% better than PNG, JPEG 2000, JPEG-lossless, and LOCO, respectively. Besides, for the same images, the proposed method offers near lossless compression at relatively low computational cost, providing an average bit rate of 2.57 bits/pixel and PSNR of 47.77 dB.

For image compression, it is very necessary that the selection of transform should reduce the size of the resultant data as compared to the original data set. In this paper, a new lossless image compression method is proposed. For continuous and discrete time cases, wavelet transform and wavelet packet transform has emerged as popular techniques. While integer wavelet using the lifting scheme significantly reduces the computation time, we propose a completely new approach for further speeding up the computation. First, wavelet packet transform (WPT) and lifting scheme (LS) are described. Then an application of the LS to WPT is presented which leads to the generation of integer wavelet packet transform (IWPT). The proposed method, Integer Wavelet Packet Transform (IWPT) yields a representation which can be lossless, as it maps an integer valued sequence onto the integer valued coefficients. The idea of Wavelet Packet Tree is used to transform the still and color images. IWPT tree can be built by iterating the single wavelet decomposition step on both the low-pass and high-pass branches, with rounding off in order to achieve the integer transforms. Thus, the proposed method provides good compression ratio.

Today is the digital era and the human being is surrounded by digital gadgets. Now a days, photography is a part of human being's daily life and digital images are widely used in computer applications. As the megapixels of the digital cameras are increasing, more storage memory is required and at the same time more bandwidth is needed for transmission of digital images. This results in need of image compression. This paper explains the use of Legendre polynomials for lossy and lossless image compression. Different approximations for image transformation have been evaluated as 1-D Legendre polynomials, 1-D adaptive Legendre polynomials, 2-D Legendre polynomials and 2-D adaptive Legendre polynomials. Moreover, the performance of different image scanning methods have been tested. Results have been compared in terms of peak signal-to-noise ratio (PSNR), nominal compression rate (NCR), mean structural similarity (MSSIM) and compression ratio in order to minimize the difference between the approximated polynomial output and the original pixel gray level.