Backward-adaptive lossless compression of video sequences

Rundbrief Der Gi-fachgruppe 5.10 Informationssystem-architekturen, 1991

Lossless data compression systems allow an exact replica of the original data to be reproduced at the receiver. Lossless compression has found a wide range of applications in such diverse fields as: compression of computer data, still images (e.g., medical or graphical images) and video (usually, in the form of entropy coding of the output of intra/inter-frame lossy schemes). It has been studied for over forty years and new compression algorithms are still continuously developed. This paper is a survey of current lossless techniques with results quoted for both sequential data files and still images.

Journal of Electronic Imaging, 1993

Lossy plus lossless techniques for image compression split an image into a low-bit-rate lossy representation and a residual that represents the difference between this low-rate lossy image and the originaL Conventional schemes encode the lossy image and its lossless residual in an independent manner. We show that making use of the lossy image to encode the residual can lead to significant savings in bit rate. Further, the complexity increase to attain these savings is minimaL The savings are achieved by capturing the inherent structure of the image in the form of a noncausal prediction model that we call a prediction tree. This prediction model is then used to transmit the lossless residuaL Simulation results show that a reduction of 0.5 to 1.0 bit/pixel can be achieved in bit rates compared to the conventional approach of independently encoding the residuaL

IEEE Signal Processing Letters, 2000

A novel image compression technique is presented that incorporates progressive transmission and near-lossless compression in a single framework. Experimental performance of the proposed coder proves to be competitive with the state-of-the-art compression schemes.

2012 VIII Southern Conference on Programmable Logic, 2012

This paper presents the Reference Frame Context Adaptive Variable-Length Compressor (RFCAVLC) for video coding systems. RFCAVLC aims to reduce the external memory bandwidth required to carry out this process. Six experiments were performed, all based on adaptations of the Huffman algorithm, and the best experiment achieved an average compression rate of more than 24% without any loss in quality for all targeted resolutions. This result is similar to the best solutions proposed in the literature, but it is the only one without losses in this process. The presented RFCAVLC splits the reference frames in 4x4 blocks and compresses these blocks using one of four static code tables in a context-adaptive way. An architecture that implements the encoder of the RFCAVLC solution was described in VHDL and synthesized to an Altera Stratix IV FPGA. The synthesis results achieved by the designed architecture indicate that this solution can be easily coupled to a complete video encoder system with negligible hardware overhead and without compromising throughput.

Visual Communications and Image Processing 2004, 2004

for their helpful comments and support. I am also grateful to Haoping Yu, who is part of the Corporate Research Group at Thomson in Indianapolis. Haoping and I worked together on a summer internship in 2002 where he gave me many inspirational comments about my research direction. I would also like to thank colleagues in the VIPER (Video and Image Processing) Lab for sharing time in discussions, especially Jinwha Yang who gave many helpful comments concerning research techniques. My family has fully supported me throughout my years at Purdue. Jong-Hun (Bori) Park, Min-Seo (Sori) Park and especially my wife Jin-Hee Cho are my most valuable assets and I am eternally grateful for their emotional steadfastness. Last, but not least, I would like to thank the support of my brother and sisters in Korea.

We present a new method for lossless image compression that gives compression comparable to JPEG lossless mode with about five times the speed. Our method, called ELICS, is based on a novel use of two neighboring pixels for both prediction and error modeling. For coding we use single bits, adjusted binary codes, and Golomb Rice codes. For the latter we present and analyze a provably good method for estimating the single coding parameter. Efficient, lossless image compression system (ELICS) algorithm, which consists of simplified adjusted binary code and Golomb–Rice code with storage-less k parameter selection, is proposed to provide the lossless compression method for high-throughput applications. The simplified adjusted binary code reduces the number of arithmetic operation and improves processing speed. According to theoretical analysis, the storage-less k parameter selection applies a fixed value in Golomb–Rice code to remove data dependency and extra storage for cumulation table.

Lossless image compression is a class of image compression algorithms that allows the original image to be perfectly reconstructed from the compressed image. This undertaking shows another lossless color image compression algorithm based on pixel prediction and arithmetic coding. Lossless squeezing of a Red, Green and Blue (RGB) picture is carried out by first decorrelating utilizing Reversible Color Transform (RCT). The got Y part is then compacted by a conventional lossless grayscale picture clamping strategy. The chrominance image is encoded utilizing Arithmetic coding and pixel prediction system. By utilizing RCT the forecast lapse is characterized and arithmetic coding is connected to the mistake signal. The packed image and encoded picture is joined to structure a lossless compacted RGB picture. It is demonstrated that this system diminishes the bit rates contrasted and JPEG 2000 and JPEG-XR. With a specific end goal to further lessen the bit rate the compression strategies and the pixel prediction strategy can be altered for better execution.

Due to size limitation and complexity of the hardware in transmission applications, multimedia systems and computer communications, compression techniques are much necessary. The reasons for multimedia systems to compress the data, large storage is required to save the compressed data, the storage devices are relatively slow which in real-time, has constrain to play multimedia data, and the network bandwidth, that has limitations to real-time data transmission. This paper presents an enhanced approach of run length coding. First the DCT applied, and the quantization done on the image to be compressed, then the modified run length coding technique has been used to compress the image losslessly. This scheme represents the occurrence of repeated zeros by RUN, and a non-zero coefficient by LEVEL. It removes the value of RUN, as for the sequence of non-zero coefficients it is zero for most of the time and for a zero present between non-zero coefficients is replaced by „0‟ which results in larger compression than RUN, LEVEL (1, 0) pair is used.

Sixth Multidimensional Signal Processing Workshop

Arithmetic coding is applied to provide lossless and loss-inducing compression of optical, infrared, and synthetic aperture radar imagery of natural scenes. Arithmetic coding algorithms successfully exploit the dependence structure of images through the adaptive estimation of probability distributions conditioned on pixel contexts. Several different contexts are considered, including both predictive and non-predictive variations, with both image-dependent and image-independent variations. In lossless coding experiments, arithmetic coding algorithms are shown to outperform comparable variants of both Huffman and Lempel-Ziv-Welch coding algorithms by approximately 0.5 bits per pixel. For image-dependent contexts constructed from high-order autoregressive predictors, arithmetic coding algorithms provide compression ratios as high as 4. Contexts constructed from lower-order auteregressive predictors provide compression ratios nearly as great as those of the higher-order predictors with favorable computational trades. Compression performance variations are shown to reflect the inherent sensor-dependent differences in the stochastic structure of the imagery. Arithmetic coding is also demonstrated to be a valuable addition to loss-inducing compression techniques. Code sequences derived from a lapped orthogonal transform-based vector quantization scheme are shown to be losslessly compressible using the arithmetic coding scheme. For imagery compressed to 0.5 bits per pixel, the addition of an arithmetic coder with Markov-dependent context results in additional compression ratio gains as high as 2 with no additional loss in fidelity. 'Rome Air Development Center sponsored this work under contract number F30602-87-(3-0225.

2007 IEEE International SOC Conference, 2007

In this paper we present a novel hardware architecture for context-based statistical lossless image compression, as part of a dynamically reconfigurable architecture for universal lossless compression. A gradient-adjusted prediction and context modeling algorithm is adapted to a pipelined scheme for low complexity and high throughput. Our proposed system improves image compression ratio while keeping low hardware complexity. This system is designed for a Xilinx Virtex4 FPGA core and optimized to achieve a 123 MHz clock frequency for real-time processing.