基于人眼视觉特性加权的率失真优化编码算法

本文提出了一种基于人眼视觉特性加权的率失真优化的嵌入式静态图像编码算法。率失真优化是按照率失真斜率递减的顺序编码系数位,最先编码的位具有最大的率失真斜率,每编码一位都会使失真减少最大。率失真斜率的计算仅仅是利用MQ自适应算术编码器的概率状态估计表而进行的查表过程,率失真门限方法的编码速度比搜索最大的率失真斜率更快。基于人眼视觉特性的加权因子改变编码位序而不是编码值,该算法有更快的编码速度以及较好的压缩效果。     

基于率失真优化和PSB系数位移的ROI编码新方法

小波系数移位和率失真优化的比率控制是实现ROI编码常用的两种策略.小波系数移位的方法能准确的恢复选择的ROI区域,但会影响编码的效率.而率失真优化的方法有好的编码效率但ROI区域重建精度受到影响.将两种机制结合,本文提出了一种率失真斜率提升和PSB系数移位相结合的ROI编码新方法.实验结果表明,新算法在同等编码效率及计算复杂度下,获得了良好的ROI重建精度.     

JPEG2000算法中基于有界输入有界输出(BIBO)增益控制的小波变换定点实现技术

该文提出一种在JPEG2000算法中兼容5/3小波变换和9/7小波变换高效硬件定点实现技术。所提出的技术使用9/7提升小波变换的有界输入有界输出(Bounded Input Bounded Output, BIBO)增益来确定小波变换中间值的存储位深,使用5/3提升小波变换的BIBO增益来确定9/7提升小波变换中量化参数的选择方式和量化的实现方式,最终使用同一存储空间来存放定点5/3提升小波变换和定点9/7提升小波变换系数。该文提出的技术不仅大大节省了JPEG2000算法中小波实现模块中的硬件存储资源和算法计算量,而且也节省了后续基于上下文的位平面算术编码模块和率失真优化截取模块的存储资源和算法计算量。     

截断点可行性化的率失真优化算法

该文提出了截断点可行性化的率失真优化算法,该算法根据率失真斜率最陡下降准则对精细编码过程相关联的截断点进行重新排序。新算法较JPEG2000减少了超过50%的非可行性截断点;获得比JPEG2000更精细的嵌入式比特流;相同的压缩比下重建图像的PSNR比JPEG2000高0.1dB~0.2dB;耗费的比特平面位编码时间少于JPEG2000。     

一种使用空频分割法的静止彩色图像压缩方案研究

采用一种高效的空频分割法(SFS)作为一种图像分割方案,然后选择一个合适的的熵编码算法与开发的分割方案一起来改善图像压缩性能。主要涉及自适应小波编码技术与空间编码的创新方案。实验结果表明建议的空间熵编码器比基本的算术编码能产生更好的效果,空间-频率分割比基本的算术编码实现了更佳的率失真优化。研究了建议的压缩系统包含的编码控制参数的大小范围。     

一种基于MQ编码器的图像联合压缩加密算法

为实现图像的压缩和加密同步,使用MQ编码器对内嵌零树小波压缩算法进行改进,将混合混沌序列作为流密钥对比特平面编码生成的上下文和判决进行修正,并送入MQ编码器进行熵编码。对算法进行仿真,结果表明:与原压缩算法相比,所提出算法的重构图像PSNR值至少提高了1 dB,且抗攻击性好,加解密速度快。算法实现了分辨率选择性加密,并在数据压缩的同时实现了算术加密。     

率失真优化的压缩感知图像编码

针对基于压缩感知的图像编码系统,分析了系统中编码参数和码率以及失真的关系,在此基础上提出了基于压缩感知的图像编码系统的码率-失真模型.根据所提模型设计了率失真优化的压缩感知图像编码算法.在给定码率的条件下,优化编码参数,使得编码器失真最小.算法在Matlab的编码平台上进行了仿真和实验,结果证明提出的码率-失真模型能够很好地拟合实际率失真曲线,并且基于该模型的率失真优化算法有效的提高了压缩感知图像编码系统的性能.     

基于均匀网格编码量化的超光谱图像自适应压缩

提出一种基于小波系数分类的超光谱图像压缩方法．算法首先将各波段小波分解并将所得子带划分成子块,而后根据子块活动性将其分类．在分类基础上,使用预测差分技术去除谱间冗余,此过程中分别求取各子类的预测系数以反映子带的局部相关性,而后利用均匀网格编码量化方法来量化残差系数序列,最后使用自适应算术编码对量化码字进行熵编码,为使编码器能在所有系数序列中最优地分配比特,本文提出一个基于序列统计特性和网格编码量化器率-失真特性的比特分配算法,实验证明该方法能高效地压缩超光谱图像,表现出优异的压缩性能。     

一种基于小波的多光谱图像压缩方法

文章提出一种基于Karhunen2Loeve变换(KLT)和小波量化编码的多光谱图像压缩方法。该法首先使用KL变换步骤来去除谱间冗余,而后对各变换波段图像进行小波变换,并利用均匀阈值网格编码量化方法来量化小波子带图像,最后使用算术编码对量化结果进行熵编码。为使编码器能为所有谱段各子带获取率- 失真意义上最优的量化阈值,本文提出基于子带图像统计特性和网格编码量化器率- 失真特性的比特分配算法。实验表明,本方法能高效地压缩多光谱图像,表现出优异的压缩性能。     

一种H.264视频流自适应率失真优化编码算法

为了提高编码视频流在丢包网络环境中的抗误码性能,目前比较常用的是采用帧内刷新算法.在率失真框架之内的帧内优化编码刷新算法,则被认为是更为直接和有效的解决办法.在视频编码标准H.264/JVT中采用的就是这种算法.然而由于没有考虑到信道丢包率对编码器端进行仿真解码次数的影响,从而导致在进行率失真优化编码时的计算量较大,编码耗时较长,严重影响了编码器的编码效率.基于以上分析,提出了一种改进的自适应率失真优化编码算法.将H.264标准率失真优化编码算法中计算解码器端视频帧期望失真度的代数平均值算法,改进为加权平均值算法.仿真实验表明,提出的算法可根据信道丢包率和模拟信道状态个数信息来自适应地决定编码器端进行仿真解码计算的次数,从而有效降低H.264标准率失真优化编码算法中的计算冗余和计算复杂度,节省编码耗时.在模拟信道状态个数默认为30个时,本算法最多可节省近55%的编码耗时.     

An embedded still image coder with rate-distortion optimization

It is well known that the fixed rate coder achieves optimality when all coefficients are coded with the same rate-distortion (R-D) slope. In this paper, we show that the performance of the embedded coder can be optimized in a rate-distortion sense by coding the coefficients with decreasing R-D slope. We denote such a coding strategy as rate-distortion optimized embedding (RDE). RDE allocates the available coding bits first to the coefficient with the steepest R-D slope, i.e., the largest distortion decrease per coding bit. The resultant coding bitstream can be truncated at any point and still maintain an optimal R-D performance. To avoid the overhead of coding order transmission, we use the expected R-D slope, which can be calculated from the coded bits and is available in both the encoder and the decoder. With the probability estimation table of the QM-coder, the calculation of the R-D slope can be just a lookup table operation. Experimental results show that the rate-distortion optimization significantly improves the coding efficiency in a wide bit rate range.     

Universal trellis coded quantization

A new form of trellis coded quantization based on uniform quantization thresholds and "on-the-fly" quantizer training is presented. The universal trellis coded quantization (UTCQ) technique requires neither stored codebooks nor a computationally intense codebook design algorithm. Its performance is comparable with that of fully optimized entropy-constrained trellis coded quantization (ECTCQ) for most encoding rates. The codebook and trellis geometry of UTCQ are symmetric with respect to the trellis superset. This allows sources with a symmetric probability density to be encoded with a single variable-rate code. Rate allocation and quantizer modeling procedures are given for UTCQ which allow access to continuous quantization rates. An image coding application based on adaptive wavelet coefficient subblock classification, arithmetic coding, and UTCQ is presented. The excellent performance of this coder demonstrates the efficacy of UTCQ. We also present a simple scheme to improve the perceptual performance of UTCQ for certain imagery at low bit rates. This scheme has the added advantage of being applied during image decoding, without the need to reencode the original image.     

Adaptive compression of medical ultrasound images

An adaptive image-coding algorithm for compression of medical ultrasound (US) images in the wavelet domain is presented. First, it is shown that the histograms of wavelet coefficients of the subbands in the US images are heavy-tailed and can be better modelled by using the generalised Student's t-distribution. Then, by exploiting these statistics, an adaptive image coder named JTQVS-WV is designed, which unifies the two approaches to image-adaptive coding: rate-distortion (R-D) optimised quantiser selection and R-D optimal thresholding, and is based on the varying-slope quantisation strategy. The use of varying-slope quantisation strategy (instead of fixed R-D slope) allows coding of the wavelet coefficients across various scales according to their importance for the quality of reconstructed image. The experimental results show that the varying-slope quantisation strategy leads to a significant improvement in the compression performance of the JTQVS-WV over the best state-of-the-art image coder, SPIHT, JPEG2000 and the fixed-slope variant of JTQVS-WV named JTQ-WV. For example, the coding of US images at 0.5 bpp yields a peak signal-to-noise ratio gain of >0.6, 3.86 and 0.3 dB over the benchmark, SPIHT, JPEG2000 and JTQ-WV, respectively.     

基于非零小波系数比的图像传输速率控制

该文提出一种新的图像传输速率控制方案:基于非零小波系数比的线性图像传输速率控制算法。通过对图像小波变换系数中速率-失真关系曲线的分析,提出了小波变换系数中非零系数占总像素的比率与输出比特率之间的线性关系模型,并在此基础上实现X树图像传输编码的前向传输速率控制。实验表明该文提出的速率控制算法具有简单、高速、准确等优点,是一种较理想的图像传输速率控制方案。     

Context-based entropy coding of block transform coefficients for image compression

It has been well established that state-of-the-art wavelet image coders outperform block transform image coders in the rate-distortion (R-D) sense by a wide margin. Wavelet-based JPEG2000 is emerging as the new high-performance international standard for still image compression. An often asked question is: how much of the coding improvement is due to the transform and how much is due to the encoding strategy? Current block transform coders such as JPEG suffer from poor context modeling and fail to take full advantage of correlation in both space and frequency sense. This paper presents a simple, fast, and efficient adaptive block transform image coding algorithm based on a combination of prefiltering, postfiltering, and high-order space-frequency context modeling of block transform coefficients. Despite the simplicity constraints, coding results show that the proposed coder achieves competitive R-D performance compared to the best wavelet coders in the literature.     

Bit allocation for dependent quantization with applications tomultiresolution and MPEG video coders

We address the problem of efficient bit allocation in a dependent coding environment. While optimal bit allocation for independently coded signal blocks has been studied in the literature, we extend these techniques to the more general temporally and spatially dependent coding scenarios. Of particular interest are the topical MPEG video coder and multiresolution coders. Our approach uses an operational rate-distortion (R-D) framework for arbitrary quantizer sets. We show how a certain monotonicity property of the dependent R-D curves can be exploited in formulating fast ways to obtain optimal and near-optimal solutions. We illustrate the application of this property in specifying intelligent pruning conditions to eliminate suboptimal operating points for the MPEG allocation problem, for which we also point out fast nearly-optimal heuristics. Additionally, we formulate an efficient allocation strategy for multiresolution coders, using the spatial pyramid coder as an example. We then extend this analysis to a spatio-temporal 3-D pyramidal coding scheme. We tackle the compatibility problem of optimizing full-resolution quality while simultaneously catering to subresolution bit rate or quality constraints. We show how to obtain fast solutions that provide nearly optimal (typically within 0.3 dB) full resolution quality while providing much better performance for the subresolution layer (typically 2-3 dB better than the full-resolution optimal solution).     

Joint thresholding and quantizer selection for transform imagecoding: entropy-constrained analysis and applications to baseline JPEG

Striving to maximize baseline (Joint Photographers Expert Group-JPEG) image quality without compromising compatibility of current JPEG decoders, we develop an image-adaptive JPEG encoding algorithm that jointly optimizes quantizer selection, coefficient "thresholding", and Huffman coding within a rate-distortion (R-D) framework. Practically speaking, our algorithm unifies two previous approaches to image-adaptive JPEG encoding: R-D optimized quantizer selection and R-D optimal thresholding. Conceptually speaking, our algorithm is a logical consequence of entropy-constrained vector quantization (ECVQ) design principles in the severely constrained instance of JPEG-compatible encoding. We explore both viewpoints: the practical, to concretely derive our algorithm, and the conceptual, to justify the claim that our algorithm approaches the best performance that a JPEG encoder can achieve. This performance includes significant objective peak signal-to-noise ratio (PSNR) improvement over previous work and at high rates gives results comparable to state-of-the-art image coders. For example, coding the Lena image at 1.0 b/pixel, our JPEG encoder achieves a PSNR performance of 39.6 dB that slightly exceeds the quoted PSNR results of Shapiro's wavelet-based zero-tree coder. Using a visually based distortion metric, we can achieve noticeable subjective improvement as well. Furthermore, our algorithm may be applied to other systems that use run-length encoding, including intraframe MPEG and subband or wavelet coding.     

Motion-Compensated Coder

In an earlier paper, an extension of the pel recursive techniques of Netravali and Robbins [2] and Cafforio and Rocca [3] was introduced. Here a method is provided to realize the algorithm in hardware, with some approximations. The prediction error distribution allows the use of quantized variables to a lookup table of reasonable size. The algorithm is then incorporated into a simple multimode coder capable of 1.5 bits/pel on the sequence examined. The coder incorporates a spot filter, quantizer, block run length coding, and variable word length coding and subsampling. Simulation results are presented, including bit rate, buffer status, and mode control analysis.     

Novel rate-distortion analysis framework for bit rate and picturequality control in DCT visual coding

The rate-distortion (R-D) behaviour of a coding system depends on both the characteristics of the input source data and the coding algorithm. The authors introduce the new concepts of characteristic rate curves and rate curve decomposition to characterise the input source data and model the coding algorithm, respectively. Based on these concepts, a novel framework for R-D analysis is developed, which serves as an alternative to the classical R-D analysis. Based on this framework, a fast algorithm is proposed to predict the R-D curve before quantisation and coding. The proposed algorithm has very low computational complexity. However, in the extensive simulations, its relative prediction error is always less than 5%, which is very small. To the best of the authors' knowledge, this is the first algorithm which is able to accurately estimate the R-D curve before quantisation and coding. With the estimated R-D curve, it is possible to accurately control the output bit rate and picture quality for transform coding of still images and video sequences. In practical visual coding applications, with the proposed R-D estimation algorithm, the coding bit rate can be accurately matched to the available network bandwidth to guarantee the successful transmission of the coded image/video data