Probability Distribution Estimation for Autoregressive Pixel-Predictive Image Coding.

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.

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>

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

LAPAN-A3/IPB satellite is the latest Indonesian experimental microsatellite with remote sensing and earth surveillance missions. The satellite has three optical payloads, which are multispectral push-broom imager, digital matrix camera and video camera. To increase data transmission efficiency, the multispectral imager data can be compressed using either lossy or lossless compression method. This paper aims to analyze Differential Pulse Code Modulation (DPCM) method and Huffman coding that are used in LAPAN-IPB satellite image lossless compression. Based on several simulation and analysis that have been done, current LAPAN-IPB lossless compression algorithm has moderate performance. There are several aspects that can be improved from current configuration, which are the type of DPCM code used, the type of Huffman entropy-coding scheme, and the use of sub-image compression method. The key result of this research shows that at least two neighboring pixels should be used for DPCM calculation to increase compression performance. Meanwhile, varying Huffman tables with sub-image approach could also increase the performance if on-board computer can support for more complicated algorithm. These results can be used as references in designing Payload Data Handling System (PDHS) for an upcoming LAPAN-A4 satellite.

In September 1996, a collaborative experiment between the University of Victoria and the Geological Survey of Canada was carried out to investigate the marine gas hydrate layer found on the continental slope off Vancouver Island. High resolution multichannel seismic data was used to image the bottom simulating reflector (BSR), which is the base of the hydrate layer. Estimates of heat flow values were determined from the depth of the BSR. High heat flow values were observed at the flanks of topographic highs and low heat flow values were observed at the topographic highs. These indicate a channelized migration of fluids and a possible migration of BSR.