美国大联盟HDTV系统图像压缩编码方案

本文论述美国大联盟（ＧｒａｎｄＡｌｌｉａｎｃｅ）高清晰度电视系统图像压缩编码方案，主要内容包括：方案简介，压缩编码算法，兼容性问题等。

HDTV信源编码中的自适应处理

在序列图像编码中，自适应性是提高图像主观质量的一个关键，自适应处理方法一般是窈门（ｋｎｏｗ－ｈｏｗ）的形式出现，它是从编码的实践中获得的。讨论了人的视觉特性及如何将它用于对ＤＣＴ系数的量化，以改善ＨＤＴＶ序列解码象的主观质量。实验表明，文中所述方法的解码图像的主观质量比作者在文献［１］中报告的有明显改善。

数字HDTV的三个关键问题

本文论述高清晰度电视（ＨＤＴＶ）的三个关键问题：ＨＤＴＶ制式、图像信源编码、信道编码与传输。

基于小波分析的HDTV信源编码技术

分析研究了基于MPEG-2标准的高清晰度数字电视信源编码技术,阐述了小波分析中多分辨分析的基本思想和理论,并将基于多分辨思想的Mallat算法较好地应用于HDTV的图像压缩编码系统中。

高清晰度电视(HDTV)的研究进展

简要回顾了高清晰度电视（HDTV）的发展进程，概述了HDTV的特点及其关键技术，介绍了国际上HDTV的研制状况及趋向，以及我国开发HDTV的进展．

Edge Oriented Compression Coding on Image Sequence

An edge oriented image sequence coding scheme is presented. On the basis of edge detecting, an image could be divided into the sensitized region and the smooth region. In this scheme, the architecture of sensitized region is approximated with linear type of segments. Then a rectangle belt is constructed for each segment. Finally, the gray value distribution in the region is fitted by normal forms polynomials. The model matching and motion analysis are also based on the architecture of sensitized region. For the smooth region we use the run length scanning and linear approximating. By means of normal forms polynomial fitting and motion prediction by matching, the images are compressed. It is shown through the simulations that the subjective quality of reconstructed picture is excellent at 0.0075 bit per pel.

Second Generation Wavelet Applied to Lossless Compression Coding of Image

In this paper, the second generation wavelet transform is applied to image lossless coding, according to its characteristic of reversible integer wavelet transform. The second generation wavelet transform can provide higher compression ratio than Huffman coding while it reconstructs image without loss compared with the first generation wavelet transform. The experimental results show that the se cond generation wavelet transform can obtain excellent performance in medical image compression coding.

An efficient adaptive arithmetic coding image compression technology

This paper proposes an efficient lossless image compression scheme for still images based on an adaptive arithmetic coding compression algorithm. The algorithm increases the image coding compression rate and ensures the quality of the decoded image combined with the adaptive probability model and predictive coding. The use of adaptive models for each encoded image block dynamically estimates the probability of the relevant image block. The decoded image block can accurately recover the encoded image according to the code book information. We adopt an adaptive arithmetic coding algorithm for image compression that greatly improves the image compression rate. The results show that it is an effective compression technology.

METHOD OF VECTOR QUANTIZATION CODING USING RECTANGULAR TRANSFORM FOR IMAGE COMPRESSION

First of all a simple and practical rectangular transform is given,and then thevector quantization technique which is rapidly developing recently is introduced.We combinethe rectangular transform with vector quantization technique for image data compression.Thecombination cuts down the dimensions of vector coding.The size of the codebook can reasonablybe reduced.This method can reduce the computation complexity and pick up the vector codingprocess.Experiments using image processing system show that this method is very effective inthe field of image data compression.

Screen image sequence compression method utilizing adaptive block size coding and hierarchical GOP structure

To compress screen image sequence in real-time remote and interactive applications,a novel compression method is proposed.The proposed method is named as CABHG.CABHG employs hybrid coding schemes that consist of intra-frame and inter-frame coding modes.The intra-frame coding is a rate-distortion optimized adaptive block size that can be also used for the compression of a single screen image.The inter-frame coding utilizes hierarchical group of pictures(GOP) structure to improve system performance during random accesses and fast-backward scans.Experimental results demonstrate that the proposed CABHG method has approximately 47%-48% higher compression ratio and 46%-53% lower CPU utilization than professional screen image sequence codecs such as TechSmith Ensharpen codec and Sorenson 3 codec.Compared with general video codecs such as H.264 codec,XviD MPEG-4 codec and Apple's Animation codec,CABHG also shows 87%-88% higher compression ratio and 64%-81% lower CPU utilization than these general video codecs.

Complete Focal Plane Compression Based on CMOS Image Sensor Using Predictive Coding

In this paper, a CMOS image sensor(CIS) is proposed, which can accomplish both decorrelation and entropy coding of image compression directly on the focal plane. The design is based on predictive coding for image decorrelation. The predictions are performed in analog domain by 2×2 pixel units. Both the prediction residuals and original pixel values are quantized and encoded in parallel. Since the residuals have a peak distribution around zero,the output codewords can be replaced by the valid part of the residuals' binary mode. The compressed bit stream is accessible directly at the output of CIS without extra disposition. Simulation results show that the proposed approach achieves a compression rate of 2. 2 and PSNR of 51 on different test images.

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

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.

Lossy-to-Lossless Compression of Hyperspectral Image Using the 3D Set Partitioned Embedded ZeroBlock Coding Algorithm

In this paper, we propose a three-dimensional Set Partitioned Embedded ZeroBlock Coding (3D SPEZBC) lossy-to-lossless compression algorithm for hyperspectral image which is an improved three-dimensional Embedded ZeroBlock Coding (3D EZBC) algorithm. The algorithm adopts the 3D integer wavelet packet transform proposed by Xiong et al. to decorrelate, the set-based partitioning zeroblock coding to process bitplane coding and the con-text-based adaptive arithmetic coding for further entropy coding. The theoretical analysis and experimental results demonstrate that 3D SPEZBC not only provides the same excellent compression performances as 3D EZBC, but also reduces the memory requirement compared with 3D EZBC. For achieving good coding performance, the diverse wave-let filters and unitary scaling factors are compared and evaluated, and the best choices were given. In comparison with several state-of-the-art wavelet coding algorithms, the proposed algorithm provides better compression performance and unsupervised classification accuracy.

Medical Image Compression Using Wrapping Based Fast Discrete Curvelet Transform and Arithmetic Coding

Due to the development of CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), EBCT (Electron Beam Computed Tomography), SMRI (Stereotactic Magnetic Resonance Imaging), etc. has enhanced the distinguishing rate and scanning rate of the imaging equipments. The diagnosis and the process of getting useful information from the image are got by processing the medical images using the wavelet technique. Wavelet transform has increased the compression rate. Increasing the compression performance by minimizing the amount of image data in the medical images is a critical task. Crucial medical information like diagnosing diseases and their treatments is obtained by modern radiology techniques. Medical Imaging (MI) process is used to acquire that information. For lossy and lossless image compression, several techniques were developed. Image edges have limitations in capturing them if we make use of the extension of 1-D wavelet transform. This is because wavelet transform cannot effectively transform straight line discontinuities, as well geographic lines in natural images cannot be reconstructed in a proper manner if 1-D transform is used. Differently oriented image textures are coded well using Curvelet Transform. The Curvelet Transform is suitable for compressing medical images, which has more curvy portions. This paper describes a method for compression of various medical images using Fast Discrete Curvelet Transform based on wrapping technique. After transformation, the coefficients are quantized using vector quantization and coded using arithmetic encoding technique. The proposed method is tested on various medical images and the result demonstrates significant improvement in performance parameters like Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).

Chaos-based encryption for fractal image coding

A chaos-based cryptosystem for fractal image coding is proposed. The Renyi chaotic map is employed to determine the order of processing the range blocks and to generate the keystream for masking the encoded sequence. Compared with the standard approach of fraetal image coding followed by the Advanced Encryption Standard, our scheme offers a higher sensitivity to both plaintext and ciphertext at a comparable operating efficiency. The keystream generated by the Renyi chaotic map passes the randomness tests set by the United States National Institute of Standards and Technology, and so the proposed scheme is sensitive to the key.

An Adaptive Block-Based Fractal Image Coding Scheme

Based on Jacquin's work. this paper presents an adaptive block-based fractal image coding scheme. Firstly. masking functions are used to classify range blocks and weight the mean Square error (MSE) of images. Secondly, an adaptive block partition scheme is introduced by developing the quadtree partition method. Thirdly. a piecewise uniform quantization strategy is appled to quantize the luminance shifting. Finally. experiment results are shown and compared with what reported by Jacquin and Lu to verify the validity of the methods addressed by the authors.

Low complexity DCT-based distributed source coding with Gray code for hyperspectral images

To compress hyperspectral images, a low complexity discrete cosine transform （DCT）-based distributed source coding （DSC） scheme with Gray code is proposed. Unlike most of the existing DSC schemes, which utilize transform in spatial domain, the proposed algorithm applies transform in spectral domain. Set-partitioning-based approach is applied to reorganize DCT coefficients into waveletlike tree structure and extract the sign, refinement, and significance bitplanes. The extracted refinement bits are Gray encoded. Because of the dependency along the line dimension of hyperspectral images, low density paritycheck-（LDPC）-based Slepian-Wolf coder is adopted to implement the DSC strategy. Experimental results on airborne visible/infrared imaging spectrometer （AVIRIS） dataset show that the proposed paradigm achieves up to 6 dB improvement over DSC-based coders which apply transform in spatial domain, with significantly reduced computational complexity and memory storage.

Recent Progress in Image Coding

ＲｅｃｅｎｔＰｒｏｇｒｅｓｉｎＩｍａｇｅＣｏｄｉｎｇＦａｎＣｈａｎｇｘｉｎＬｕＺｈａｏｙａｎｇ（ＸｉｄｉａｎＵｎｉｖｅｒｓｉｔｙ，Ｘｉ’ａｎ７１００７１）ＡｂｓｔｒａｃｔＴｈｅｗｏｒｋｓｏｎｉｍａｇｅｃｏｄｉｎｇｉｎｒｅｃｅｎｔｔｗｏｙｅ...

An efficient BTC image compression algorithm with visual patterns

Discusses block truncation coding (BTC) a simple and fast image compression technique suitable for real time image transmission with high channel error resisting capability and good reconstructed image quality, and its main drawback of high bit rate of 2 bits/pixel for a 256 gray image for the purpose of reducing the bit rate, and introduces a simple look up table method for coding the higher mean and the lower mean of a block, and a set of 24 visual patterns used to encode 4×4 bit plane of the high detail block and proposes a new algorithm, when needs only 19 bits to encode 4×4 high detail block and 12 bits to encode the 4×4 low detail block.

AN EFFICIENT BTC IMAGE COMPRESSION ALGORITHM

Block truncation coding (BTC) is a simple and fast image compression technique suitable for real time image transmission, and it has high channel error resisting capability and good reconstructed image quality. The main shortcoming of the original BTC algorithm is the high bit rate (normally 2 bits/pixel). In order to reduce the bit rate, an efficient BTC image compression algorithm was presented in this paper. In the proposed algorithm, a simple look up table method is presented for coding the higher mean and the lower mean of a block without any extra distortion, and a prediction technique is introduced to reduce the number of bits used to code the bit plane with some extra distortion. The test results prove the effectiveness of the proposed algorithm.