Context Clustering in Lossless Compression of Gray-Scale Image

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>

This paper presents a new method of lossless image compression. An image is characterized by homogeneous parts. The bit planes, which are of high weight are characterized by sequences of 0 and 1 are successive encoded with RLE, whereas the other bit planes are encoded by the arithmetic coding (AC) (static or adaptive model). By combining an AC (adaptive or static) with the RLE, a high degree of adaptation and compression efficiency is achieved. The proposed method is compared to both static and adaptive model. Experimental results, based on a set of 12 gray-level images, demonstrate that the proposed scheme gives mean compression ratio that are higher those compared to the conventional arithmetic encoders.

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.

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.

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.

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.

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.

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.

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.

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.

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.

With advancement of technology, the image processing techniques play an important role. Image processing is required to improve the pictorial information for human perception and for autonomous machine applications. It finds application in various fields such as quality control, remote sensing, imaging science, etc. It also finds application in areas where efficient storage and transmission of images is necessary, Say for example; we want to store an image in the computer with less disk space. Also, in cases where we want to signal an image or video on a transmission medium with less bandwidth communication channel. Images are usually in the form of matrices and an uncompressed image uses huge number of bytes for storage. Image compression is used to reduce the size of the bitmap without compromising on the quality of an image. This enables to store large number of images in the same given amount of storage space. Image compression will also reduce the time taken to transmit or to download images from the internet. Many methods are available to compress an image file. The image compression methods which have gained popularity are the transform based coding methods like Discrete Cosine Transform (DCT), discrete wavelet Transform (DWT) and fractals. However these methods have drawbacks like low compression ratio and high encoding time. In this paper a new hybrid methodology is proposed by combining lossy and lossless compression methods. The proposed hybrid technique combines DCT and fractal quadtree decomposition with Huffman encoding of threshold value 0.2. The results obtained from the proposed method are then compared with state-of-the-art compression methods.

In High Dynamic Range Images (HDR), the illumination intensity levels vary over a very wide range and hence the storage space required is very large compared to normal Low Dynamic Range images. Transmission and distribution of HDR images and videos demand a large bandwidth coupled with higher communication overheads. Hence, image compression is invariably adopted for HDR images/videos so that they can be used efficiently in communication-intensive scenarios such as video conferencing and mobile applications. In this paper, a new method of lossless HDR image compression is presented that uses modulo encoding and subsequent reconstruction.

EASC: An exception-aware semantic compression framework for real-world knowledge graphs