超光谱图像的分布式压缩

针对超光谱图像压缩进行了研究,提出了一种有效的基于分布式信源编码(Distributed Source Coding, DSC)的有损压缩算法。该算法利用多元陪集码和标量量化的方式实现超光谱图像的分布式有损压缩,针对分布式信源编码,利用多波段预测的方式为每个编码块构造边信息,然后采用标量量化的方式对编码块和其边信息同时进行量化处理。根据分布式信源编码原理,给出了各编码块量化后的编码码率。为了减少标量量化带来的信息丢失,算法引入了跳跃策越。部分均方误差意义上损失较大的编码块将由其边信息直接代替。实验结果表明,所提出的算法性能与基于小波变换的算法性能相当;此外,该算法复杂度较低,适合星载超光谱图像的压缩。     

DSC的超光谱图像无损压缩算法

有效的星载超光谱图像压缩技术对于解决超光谱图像实时传输极为重要。针对超光谱图像传统的联合编解码算法的不足,提出了一种基于分布式信源编码(Distributed Source Coding,DSC)的超光谱图像无损压缩算法。为利用超光谱图像的局部空间相关性,将超光谱图像进行分块处理;引入多元线性回归模型构建编码块的边信息,并为每个编码块选取最优的预测阶数,以有效利用超光谱图像的局部谱间相关性。根据(n,k)线性分组码的原理,通过多元陪集码实现超光谱图像的分布式无损压缩。实验结果表明:该算法能够取得较好的无损压缩性能,同时具有较低的编码复杂度,适合星载超光谱图像的压缩实现。     

基于聚类的高光谱图像无损压缩

高光谱海量数据的有效压缩成为遥感技术发展中需要迫切解决的问题。该文提出了一种基于聚类的高光谱图像无损压缩算法。针对高光谱图像不同频谱波段间相关性不同的特点,根据相邻波段相关性大小进行波段分组。由于高光谱图像波段数量较多,采用自适应波段选择算法对高光谱图像进行降维,以获取信息量较大的部分波段,利用k均值算法对降维后的波段谱矢量进行聚类。采用多波段预测的方案对各组中的波段进行预测,对于各个分类中的每个像素,分别选取与其空间相邻的已编码的部分同类点进行训练,从而获得当前像素的谱间最优预测系数。对AVIRIS型高光谱图像的实验结果表明,该算法可显著降低压缩后的平均比特率。     

基于L∞最小搜索和陪集码的高光谱图像无损及近无损压缩

 分布式信源编码(DSC)由于其较低的编码复杂度及较高的抗误码性被应用于高光谱图像压缩.在典型的基于陪集码的分布式高光谱图像无损压缩算法s-DSC(scalar coset DSC)框架下,本文指出最优的预测准则应为无穷范数最小,提出了基于L_∞最小搜索的预测方法来逼近最优准则,并将框架推广到近无损压缩.实验表明,和原有的s-DSC相比,本文算法无损压缩的平均码率降低了大约0.25bpp,近无损性能也明显优于JPEG-LS,本文算法具有较低的计算复杂度、较高的压缩性能,且具有一定的抗误码能力,适用于星上压缩.     

基于同类邻点预测的高光谱图像无损压缩

提出了一种基于聚类、对类波段重排、搜索同类相邻点预测的高光谱数据无损压缩方法。根据谱向特征,进行高光谱图像按像素聚类,对各个分类,根据波段间相关系数,利用最大生成树进行波段重排。对每种分类像素,搜索同类邻点,训练预测系数,并进行三维预测。残差采用Golomb-Rice编码。实验证实了算法的有效性。     

基于分类非线性预测的高光谱图像无损压缩

高光谱数据的有效压缩成为遥感技术发展中需要迫切解决的问题.提出了一种基于分类非线性预测的高光谱图像无损压缩算法.针对不同频谱波段间相关性不同的特点,根据相邻波段相关性大小进行波段分组.为提高谱间预测性能,对各组波段进行最优排序.采用自适应波段选择算法对高光谱图像进行降维,并利用k-means算法对降维后波段的谱向矢量进行分类.在参考波段和预测波段中选取具有相同空间位置的上下文结构,在分类结果的基础上,对当前波段进行谱间非线性预测.参考波段采用JPEG-LS标准进行压缩,预测残差进行Golomb-Rice编码.对AVIRIS型高光谱图像的实验结果表明,该算法可显著降低压缩后的平均比特率.     

基于稀疏表示的高光谱数据压缩算法

如何降低高光谱图像大规模数据的存储和传输代价一直是学者们关心的问题。该文提出一种基于稀疏表示的高光谱数据压缩算法,通过一种波段选择算法构造训练样本集合,利用训练得到的基函数字典对高光谱数据所有波段进行稀疏编码,并对表示结果中非零元素的位置和数值进行量化和熵编码,从而实现高光谱图像压缩。实验结果表明该文算法与3维小波相比具有更好的非线性逼近性能,其率失真性能明显优于3D-SPIHT,并且在光谱信息保留上具有巨大的优势。     

基于波段分组的高光谱图像无损压缩

高光谱图像海量数据给存储和传输带来极大困难,必须对其进行有效压缩。针对高光谱图像不同频谱波段间相关性不同的特点,提出一种基于波段分组的高光谱图像无损压缩算法。为了降低波段排序算法的计算量,根据相邻波段相关性大小预先进行分组,采用最佳后向排序算法对各组波段进行重新排序。在当前波段和参考波段中选取具有相同空间位置的邻域结构,在最小二乘准则下,利用邻域像素对当前预测像素进行最优谱间预测。参考波段和预测残差数据进行JPEG-LS压缩。对OMIS-I型高光谱图像进行实验的结果表明,与基于多波段预测算法相比,该算法可进一步降低压缩后的平均比特率。     

基于搜索最优双预测波段的超光谱遥感图像无损压缩

该文针对超光谱各波段成像图像之间的相关性强弱程度互不相同这一特点,提出了基于搜索最优双预测波段的超光谱图像无损压缩算法。该算法通过建立一个搜索最优双预测波段的二叉树模型,搜索与每一波段相关性最强的两个波段,并用这两个波段对该波段进行预测。实验表明,与目前研究的其它超光谱图像压缩算法相比,该算法有着很好的压缩性能。     

基于双向预测的高光谱图像无损压缩

提出了一种基于双向预测的高光谱图像无损压缩算法。该算法首先采用自适应波段选择算法选出信息量较大的波段,然后利用聚类算法对这些波段的谱向矢量进行分类预处理。为了便于组织谱间预测过程,根据相邻波段相关性大小进行自适应波段分组,采用双向预测的方法去除谱间相关性。通过在参考波段和预测波段中定义三维上下文预测结构,在聚类结果的基础上,对各个像素分别训练最优的预测系数,从而实现当前波段的有效预测。对AVIRIS型高光谱图像的实验结果表明,该方法可获得较好的无损压缩性能。     

Improved SAP based on adaptive directional prediction for HEVC lossless intra prediction

In HEVC (High Efficiency Video Coding), linear interpolation of the boundary pixels is used as the predictor and pixels within the same PU (Prediction Unit) are in the same prediction direction. When the PU block is large and in high complexity, the prediction performance would worsen. Although many algorithms use the pixel-based weighted averaging or interpolation operations to perform the prediction which improves the performance of bitrate saving notably, there is still space for further improvement. This paper proposed an improved SAP (Sample-based Angular Prediction) algorithm based on adaptive directional prediction (ADSAP). The innovation of this paper lies in two aspects: it puts forward a new method to estimate the best prediction direction of current pixel and it introduces the concept of “frame-level pretreatment” which could greatly improve the encoding speed. Experimental results show that it could save the output bitrate by about 9.4158% when compared with the HEVC lossless intra prediction algorithm, which is better than other typical algorithms. Besides, the encoding and decoding time are reduced by about 11%. Moreover, when the minimum PU size gets larger, the increase of output bitrate of ADSAP is much less than that of HEVC and the conventional SAP algorithm, which makes the proposed algorithm quite suitable for the compression of high resolution videos.     

Cross residual transform for lossless intra-coding for HEVC

A new lossless intra-coding method based on a cross residual transform is applied to the next generation video coding standard HEVC (High Efficiency Video Coding). HEVC includes a multi-directional spatial prediction method to reduce spatial redundancy by using neighboring pixels as a prediction for the pixels in a block of data to be encoded. In the new lossless intra-coding method, the spatial prediction is performed as pixelwise DPCM but is implemented as block-based manner by using cross residual transform on the HEVC standard. The experimental results show that the new lossless intra-coding method reduces the bit rate of approximately 8.43% in comparison with the lossless-intra coding method in the HEVC standard and the proposed method results in slightly better compression ratio than the JPEG200 lossless coding.     

基于轻量级全连接网络的H.266/VVC分量间预测

新一代视频编码标准H.266/VVC引入分量间线性模型(CCLM)预测提高压缩效率。针对亮度色度分量存在相关性却难以建模的问题,提出基于神经网络的分量间预测算法。该算法根据待预测像素与参考像素的亮度差遴选出相关性强的参考像素构成参考子集,然后将参考子集送入轻量级全连接网络获得色度预测值。实验结果表明,与H.266/VVC测试模型版本10.0(VTM10.0)相比,所提算法可提高色度预测准确度,在Y、Cb和Cr上可分别节省0.27%、1.54%和1.84%的码率。所提算法具有不同块尺寸和编码参数均可使用统一网络结构的优点。     

基于轻量级全连接网络的H.266/VVC分量间预测

新一代视频编码标准H.266/VVC引入分量间线性模型(CCLM)预测提高压缩效率。针对亮度色度分量存在相关性却难以建模的问题,提出基于神经网络的分量间预测算法。该算法根据待预测像素与参考像素的亮度差遴选出相关性强的参考像素构成参考子集,然后将参考子集送入轻量级全连接网络获得色度预测值。实验结果表明,与H.266/VVC测试模型版本10.0(VTM10.0)相比,所提算法可提高色度预测准确度,在Y、Cb和Cr上可分别节省0.27%、1.54%和1.84%的码率。所提算法具有不同块尺寸和编码参数均可使用统一网络结构的优点。     

Adaptive transform skipping for improved coding of motion compensated residuals

New generations of video compression algorithms, such as those included in the under development High Efficiency Video Coding (HEVC) standard, provide substantially higher compression compared to their ancestors. The gain is achieved by improved prediction of pixels, both within a frame and between frames. Novel coding tools that contribute to the gain provide highly uncorrelated prediction residuals for which classical frequency decomposition methods, such as the discrete cosine transform, may not be able to supply a compact representation with few significant coefficients. To further increase the compression gains, this paper proposes transform skip modes which allow skipping one or both 1-D constituent transforms (i.e., vertical and horizontal), which is more suitable for sparse residuals. The proposed transform skip mode is tested in the HEVC codec and is able to provide bitrate reductions of up to 10% at the same objective quality when compared with the application of 2-D block transforms only. Moreover, the proposed transform skip mode outperforms the full transform skip currently investigated for possible adoption in the HEVC standard.     

Mode dependent down-sampling and interpolation scheme for high efficiency video coding

In this paper, a mode dependent down-sampling and interpolation scheme is proposed to improve the coding efficiency of the intra prediction module. In the proposed method, we elaborately design the down-sampling structures and interpolation schemes for each directional intra prediction mode by minimizing the spatial prediction distance. The sampled pixels are predicted with a traditional directional intra prediction scheme, and the non-sampled pixels are predicted from the interpolation of their neighboring reconstructed sampling pixels. Both the residuals of the sampled and non-sampled pixels are encoded at last. Experimental results show that the proposed method achieves an average 7.52% bitrate reduction relative to KTA reference software. Since the down-sampling structure and interpolation method is only related to the intra mode, there is no additional overhead at the encoder.     

Multiview video compression with 1-D transforms

Many alternative transforms have been developed recently for improved compression of images, intra prediction residuals or motion-compensated prediction residuals. In this paper, we propose alternative transforms for multiview video coding. We analyze the spatial characteristics of disparity-compensated prediction residuals, and the analysis results show that many regions have 1-D signal characteristics, similar to previous findings for motion-compensated prediction residuals. Signals with such characteristics can be transformed more efficiently with transforms adapted to these characteristics and we propose to use 1-D transforms in the compression of disparity-compensated prediction residuals in multiview video coding. To show the compression gains achievable from using these transforms, we modify the reference software (JMVC) of the multiview video coding amendment to H.264/AVC so that each residual block can be transformed either with a 1-D transform or with the conventional 2-D Discrete Cosine Transform. Experimental results show that coding gains ranging from about 1–15% of Bjontegaard-Delta bitrate savings can be achieved.     

Rate-distortion optimized rate-allocation for motion-compensated predictive video codecs using PixelRank

Inter-frame dependencies are usually ignored in video encoder coding parameter selection. This gives a non-optimal solution and degrades the compression performance. A mathematical model to estimate the importance of each pixel on the reconstructed video quality, called PixelRank, is developed in this paper. Theoretical analysis on the parameters used for PixelRank score calculation dealing with the video coding optimization problem is also given. The PixelRank algorithm tracks the importance of each pixel and distributes the PixelRank scores. With the PixelRank scores for all the pixels, MB-based quantization parameters are adjusted accordingly. Based on this technique, the rate can be allocated more accurately according to the importance of the pixels, thus achieving better overall rate-distortion performance. Compared to the non-optimized scheme in H.264/AVC, the proposed scheme can reduce 13.53% of the average bitrate and up to 25.17% of bitrate in the simulations.