Hierarchical quantization indexing for wavelet and wavelet packet image coding

In this paper, we introduce the quantization index hierarchy, which is used for efficient coding of quantized wavelet and wavelet packet coefficients. A hierarchical classification map is defined in each wavelet subband, which describes the quantized data through a series of index classes. Going from bottom to the top of the tree, neighboring coefficients are combined to form classes that represent some statistics of the quantization indices of these coefficients. Higher levels of the tree are constructed iteratively by repeating this class assignment to partition the coefficients into larger subsets. The class assignments are optimized using a rate-distortion cost analysis. The optimized tree is coded hierarchically from top to bottom by coding the class membership information at each level of the tree. Context-adaptive arithmetic coding is used to improve coding efficiency. The developed algorithm produces PSNR results that are better than the state-of-art wavelet-based and wavelet packet-based coders in literature.     

Prioritized DCT for compression and progressive transmission ofimages

An approach is based on the block discrete cosine transform (DCT). The novelty of this approach is that the transform coefficients of all image blocks are coded and transmitted in absolute magnitude order. The resulting ordered-by-magnitude transmission is accomplished without sacrificing coding efficiency by using partition priority coding. Coding and transmission are adaptive to the characteristics of each individual image. and therefore, very efficient. Another advantage of this approach is its high progression effectiveness. Since the largest transform coefficients that capture the most important characteristics of images are coded and transmitted first, this method is well suited for progressive image transmission. Further compression of the image-data is achieved by multiple distribution entropy coding, a technique based on arithmetic coding. Experiments show that the approach compares favorably with previously reported DCT and subband image codecs.     

Multilayer subband-based video coding

A motion compensated interframe subband coding algorithm suitable for a wide range of video coding applications is described. In this approach the spectrum of each frame of video signal is first decomposed into smaller frequency bands where each can then be coded accordingly. For the best performance a combination of hybrid DCT/DPCM (discrete cosine transform/differential pulse code modulation), interframe DPCM, and intraframe PCM was considered. To preserve its hierarchical structure each band is coded independently of higher frequency bands but can share information with the lower bands. A simulation was carried out for HDTV sequences     

Block adaptive quantization of images

Results on block adaptive quantization of images using the adaptive dynamic range coding (ADRC) method are presented. In this method, the minimum and maximum intensities for each block are determined, and the pixels in the block are quantized with a uniform quantizer covering this range. The minimum and maximum values are transformed to midpoint and range values and then quantized and coded for transmission as side information. In order to reduce the visibility of blocking effects, ADRC can be applied to a subband decomposition of the image. This extension is called SADRC. Experimental results are presented for ADRC applied to the original image, as well as for four-band SADRC     

Wavelet packets and spatial adaptive intraband coding of images

In this work, we present a coding scheme based on a rate-distortion optimum wavelet packets decomposition and on an adaptive coding procedure that exploits spatial non-stationarity within each subband. We show, by means of a generalization of the concept of coding gain to the case of non-stationary signals, that it may be convenient to perform subband decomposition optimization in conjunction with intraband optimal bit allocation. In our implementation, each subband is partitioned into blocks of coefficients that are coded using a geometric vector quantizer with a rate determined on the basis of spatially local statistical characteristics. The proposed scheme appears to be simpler than other wavelet packets-based schemes presented in the literature and achieves good results in terms of both compression and visual quality.     

基于自适应小波变换的嵌入图像压缩算法

针对遥感、指纹、地震资料等图像纹理复杂丰富、局部相关性较弱等特点,文章通过实施自适应小波变换、合理确定系数扫描次序、分类量化小波系数等措施,提出了一种高效的图像压缩编码算法.仿真结果表明,相同压缩比下,本文算法的图像复原质量明显优于SPIHT算法(特别是对于纹理图像,如标准图像Barbara).     

基于第二代Bandelet变换的图像编码

Bandelet基函数可以实现几何正则图像的最佳稀疏表示,在图像压缩方面有很大潜力。为了得到更高的压缩比,在第二代Bandelet变换的研究基础上,提出了一种与Bandelet变换相适应的图像编码方案。首先对Bandelet系数采用一致量化,重新组织系数的排布方式,然后设计了一种针对Bandelet系数的上下文模型,进行基于上下文的编码。对低频子带采用CALIC模型进行编码。实验表明,该编码方法对自然图像压缩是有效的,特别是对几何流特征比较明显的图像取得了优于JPEG 2000的结果。     

Multiresolution Vector Quantization for Video Coding

In this work, we propose a coding technique that is based on the generalized block prediction of the multiresolution subband decomposition of motion compensated difference image frames. A segmentation mask is used to distinguish between the regions where motion compensation was effective and those regions where the motion model did not succeed. The difference image is decomposed into a multiresolution pyramid of subbands where the highest resolution subbands are divided into two regions, based on the information given by the segmentation mask. Only the coefficients of the regions corresponding to the motion model failure are considered in the highest resolution subbands. The remaining coefficients are coded using a multiresolution vector quantization scheme that exploits inter-band non-linear redundancy. In particular, blocks in one subimage are predicted from blocks of the adjacent lower resolution subimage with the same orientation. This set of blocks plays the role of a codebook built from coefficients inside the subband decomposition itself. Whenever the inter-band prediction does not give satisfactory results with respect to a target quality, the block coefficients are quantized using a lattice vector quantizer for a Laplacian source.     

Image subband coding using context-based classification andadaptive quantization

Adaptive compression methods have been a key component of many proposed subband (or wavelet) image coding techniques. This paper deals with a particular type of adaptive subband image coding where we focus on the image coder's ability to adjust itself "on the fly" to the spatially varying statistical nature of image contents. This backward adaptation is distinguished from more frequently used forward adaptation in that forward adaptation selects the best operating parameters from a predesigned set and thus uses considerable amount of side information in order for the encoder and the decoder to operate with the same parameters. Specifically, we present backward adaptive quantization using a new context-based classification technique which classifies each subband coefficient based on the surrounding quantized coefficients. We couple this classification with online parametric adaptation of the quantizer applied to each class. A simple uniform threshold quantizer is employed as the baseline quantizer for which adaptation is achieved. Our subband image coder based on the proposed adaptive classification quantization idea exhibits excellent rate-distortion performance, in particular at very low rates. For popular test images, it is comparable or superior to most of the state-of-the-art coders in the literature.     

Digital video compression-an overview

Some of the more commonly used video compression schemes are reviewed. They are differential pulse code modulation (DPCM), Huffman coding, transform coding, vector quantization (VQ), and subband coding. The use of motion compensation to improve compression is also discussed     

Image coding based on wavelet transform and uniform scalar dead zone quantizer

In this paper, a new wavelet transform image coding algorithm is presented. The discrete wavelet transform (DWT) is applied to the original image. The DWT coefficients are firstly quantized with a uniform scalar dead zone quantizer. Then the quantized coefficients are decomposed into four symbol streams: a binary significance map symbol stream, a binary sign stream, a position of the most significant bit (PMSB) symbol stream and a residual bit stream. An adaptive arithmetic coder with different context models is employed for the entropy coding of these symbol streams. Experimental results show that the compression performance of the proposed coding algorithm is competitive to other wavelet-based image coding algorithms reported in the literature.     

采用改进图形变换的3D点云压缩

本文提出一种采用改进图形变换的3D点云压缩算法。所提算法首先通过改进图形变换将每个块中的所有子图连接为一个图，从源头减少直流系数个数，同时用每个块所有点的均值作为直流系数以降低直流量幅值，并对去平均的颜色值进行图形变换。考虑到量化后的交流系数的零系数占比比较大，本文采用了Run-Level的编码方法对非零的交流系数进行编码。对于直流系数，本文设计了一种预测编码方法对其进行有效编码。最后,编码完的交流系数和预测残差均采用霍夫曼编码器进行熵编码。实验结果表明所提算法相比多个现有3D点云压缩算法具有更高的压缩效率。      

Rank-order polynomial subband decomposition for medical image compression

In this paper, the problem of progressive lossless image coding is addressed. A nonlinear decomposition for progressive lossless compression is presented. The decomposition into subbands is called rank-order polynomial decomposition (ROPD) according to the polynomial prediction models used. The decomposition method presented here is a further development and generalization of the morphological subband decomposition (MSD) introduced earlier by the same research group. It is shown that ROPD provides similar or slightly better results than the compared coding schemes such as the codec based on set partitioning in hierarchical trees (SPIHT) and the codec based on wavelet/trellis-coded quantization (WTCQ). Our proposed method highly outperforms the standard JPEG. The proposed lossless compression scheme has the functionality of having a completely embedded bit stream, which allows for data browsing. It is shown that the ROPD has a better lossless rate than the MSD but it has also a much better browsing quality when only a part of the bit stream is decompressed. Finally, the possibility of hybrid lossy/lossless compression is presented using ultrasound images. As with other compression algorithms, considerable gain can be obtained if only the regions of interest are compressed losslessly.     

基于三维子波变换和分级零树扫描的视频编码算法研究

本文研究了三维子波变换和分级三维零树混合视频编码算法,对图像序列体作３层２２子带时,空分解后,再对最低频子带进行４子带空间分解,对这个特殊分解的三维子波结构,推广了零树的概念,用ＨＶＳ量化模型对子波系统施加量化,用分级三维零树表示量化后的２５带子波系数,这一方案比单级零树方法节省约６％的码率,并且实现了空间和时间可尺主度化码流结构。     

An Adaptive Strategy for Hybrid Image Coding

In hybrid coding technique, the sampled data are divided into blocks ofN times Msamples. Next, each block is transformed to generate a one-dimensional transform of each line in the block. The transform coefficients are then processed by a block of DPCM encoders which uncorrelate the data in the second dimension and qnantize the uncorrelated samples using appropriate quantizers. In this study an adaptive hybrid coding technique is proposed based on using a single quantizer (A/D converter) to quantize the transform coefficients and using a variable-rate algorithm for coding the quantized coefficients. The accuracy of the A/D converter (number of bits per sample) determines the fidelity of the system. The buffercontrol algorithm controls the accuracy of the A/D converter for each block resulting in a fixed-rate encoder system. Experimental results have shown a stable buffer condition and reconstructed images with a higher fidelity than nonadaptive hybrid systems.     

Invertible temporal subband/wavelet filter banks with half-pixel-accurate motion compensation

Three-dimensional (3-D) subband/wavelet coding with motion compensation has been demonstrated to be an efficient technique for video coding applications in some recent research works. When motion compensation is performed with half-pixel accuracy, images need to be interpolated in both temporal subband analysis and synthesis stages. The resulting subband filter banks developed in these former algorithms were not invertible due to image interpolation. In this paper, an invertible temporal analysis/synthesis system with half-pixel-accurate motion compensation is presented. We look at temporal decomposition of image sequences as a kind of down-conversion of the sampling lattices. The earlier motion-compensated (MC) interlaced/progressive scan conversion scheme is extended for temporal subband analysis/synthesis. The proposed subband/wavelet filter banks allow perfect reconstruction of the decomposed video signal while retaining high energy compaction of subband transforms. The invertible filter banks are then utilized in our 3-D subband video coder. This video coding system does not contain the temporal DPCM loop employed in the conventional hybrid coder and the earlier MC 3-D subband coders. The experimental results show a significant PSNR improvement by the proposed method. The generalization of our algorithm for MC temporal filtering at arbitrary subpixel accuracy is also discussed.     

Hybrid vector quantization for multiresolution image coding

In this correspondence, we propose a coding scheme that exploits the redundancy of the multiresolution representation of images, in that blocks in one subimage are predicted from blocks of the adjacent lower resolution subimage with the same orientation. The pool of blocks used for prediction of a given subband plays the role of a codebook that is built from vectors of coefficients inside the subband decomposition itself. Whenever the prediction procedure does not give satisfactory results with respect to a target quality, the block coefficients are quantized using a geometric vector quantizer for a Laplacian source.     

基于分形和小波变换的自适应混合图像编码

待编码图像经过金字塔型离散小波变换后的系数在小波域内可以组成分层树状数据结构一个小波树，这些跨越不同分辨率的小波树之间存在一定的相似性，可以通过分形变换来描述，本文正是构造小波树的基础邮基于分形和小波变换的自适应混合图像压缩算法，实验证明，我们提出的图像压缩方法与ＪＰＥＧ相比，能够在相近的压缩比的情况下（６０：１～７０：１）使得重建图像的ＰＳＮＲ（〉２９．５ｄＢ）增加约５．４ｄＢ并且图像的主观视觉     

Analysis of linear prediction, coding, and spectral estimation fromsubbands

Linear prediction schemes make a prediction xˆ_i of a data sample x_i using p previous samples. It has been shown by Woods and O'Neil (1986) as well as Pearlman (1991) that as the order of prediction p→∞, there is no gain to be obtained by coding subband samples. This paper deals with the less well understood theory of finite-order prediction and optimal coding from subbands which are generated by ideal (brickwall) filtering of a stationary Gaussian source. We first prove that pth-order prediction from subbands is superior to pth-order prediction in the fullband, when p is finite. This fact adduces that optimal vector p-tuple coding in the subbands is shown to offer quantifiable gains over optimal fullband p-tuple coding, again when p is finite. The properties of subband spectra are analyzed using the spectral flatness measure. These results are used to prove that subband DPCM provides a coding gain over fullband DPCM, for finite orders of prediction. In addition, the proofs provide means of quantifying the subband advantages in linear prediction, optimal coding, and DPCM coding in the form of gain formulas. Subband decomposition of a source is shown to result in a whitening of the composite subband spectrum. This implies that, for any stationary source, a pth-order prediction error filter (PEF) can be found that is better than the pth-order PEF obtained by solving the Yule-Walker equations resulting from the fullband data. We demonstrate the existence of such a “super-optimal” PEF and provide algorithmic approaches to obtaining this PEF. The equivalence of linear prediction and AR spectral estimation is then exploited to show theoretically, and with simulations, that AR spectral estimation from subbands offers a gain over fullband AR spectral estimation     

On the optimality of nonunitary filter banks in subband coders

This paper investigates the energy compaction capabilities of nonunitary filter banks in subband coding. It is shown that nonunitary filter banks have larger coding gain than unitary filter banks because of the possibility of performing half-whitening in each channel. For long filter unit pulse responses, optimization of subband coding gain for stationary input signals results in a filter bank decomposition, where each channel works as an optimal open-loop DPCM system. We derive a formula giving the optimal filter response for each channel as a function of the input power spectral density (PSD). For shorter filter bank responses, good gain is obtained by suboptimal half-whitening responses, but the impact on the theoretical coding gain is still highly significant. Image coding examples demonstrate that better performance is achieved using nonunitary filter banks when the input images are in correspondence with the signal model.