基于整数小波变换的机载多光谱图像无损压缩

首先探讨了基于提升方案的整数小波变换,结合线性预测技术,提出了一种机载多光谱遥感图像的无损压缩方法。该方法通过整数小波变换减少图像的空间冗余,然后根据多光谱图像谱间的相关性对整数小波变换后的结果进行预测,并且加进了相关性判断这一环节保证了预测的有效性。算法适合并行处理和硬件实现。对机载64波段多光谱遥感图像的试验结果表明,该方法与改进的预测树方法相比其无损压缩比平均提高了16%,压缩时间缩短了三分之二。     

Piecewise DC prediction in HEVC

In this paper, we propose an improved DC prediction method for high-efficiency video coding (HEVC) intra coding. The technique involves the application of pixel-wise predictors rather than block-based predictor in the DC mode. In HEVC, the pixels of neighboring reconstructed blocks are used to support multiple directional spatial prediction modes and reduce spatial redundancy. For lossless coding, pixel-by-pixel differential pulse code modulation (DPCM) is applied to directional predictions. Consequently, residuals are reduced and coding efficiency is improved. Since DC prediction still employs block-based coding, pixel-wise DC prediction is needed for better coding efficiency. When compared to HEVC lossless intra coding, the proposed algorithm reduces the bit rate by 5.95%. Furthermore, with pixel-by-pixel DPCM, the average bit rate reduction is 10.64% when compared to HEVC lossless intra coding.     

基于ARM嵌入式系统的高保真无损音乐播放器设计

介绍了1种基于ARM11嵌入式平台和嵌入式Linux内核,界面美观、操作便捷的高保真无损音乐播放器的设计。它支持WAV、FLAC和APE3种无损压缩格式音乐;还可通过触摸屏实现浏览音乐目录,控制音乐的播放、暂停和调节播放进度等控制功能;它支持外接移动硬盘、SD卡和优盘等大容量存储设备。在开发平台上已实现采样位数16或24,采样率上限48kHz的WAV、FLAC和APE高保真音乐播放。     

Improved Lossless Data Hiding for JPEG Images Based on Histogram Modification

This paper proposes a lossless and high payload data hiding scheme for JPEG images by histogram modification. The most in JPEG bitstream consists of a sequence of VLCs (variable length codes) and the appended bits. Each VLC has a corresponding RLV (run/length value) to record the AC/DC coefficients. To achieve lossless data hiding with high payload, we shift the histogram of VLCs and modify the DHT segment to embed data. Since we sort the histogram of VLCs in descending order, the filesize expansion is limited. The paper’s key contribution includes: Lossless data hiding, less filesize expansion in identical pay-load and higher embedding efficiency.     

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。     

Optimizing LZSS compression on GPGPUs

In this paper, we present an algorithm and provide design improvements needed to port the serial Lempel–Ziv–Storer–Szymanski (LZSS), lossless data compression algorithm, to a parallelized version suitable for general purpose graphic processor units (GPGPU), specifically for NVIDIA’s CUDA Framework. The two main stages of the algorithm, substring matching and encoding, are studied in detail to fit into the GPU architecture. We conducted detailed analysis of our performance results and compared them to serial and parallel CPU implementations of LZSS algorithm. We also benchmarked our algorithm in comparison with well known, widely used programs: GZIP and ZLIB. We achieved up to 34× better throughput than the serial CPU implementation of LZSS algorithm and up to 2.21× better than the parallelized version.     

Perceptually-lossless image coding based on foveated-JND and H.265/HEVC

Removing perceptual redundancy plays an important role in image compression. In this paper we develop a foveated just-noticeable-difference (FJND) model to quantify the perceptual redundancy in the image and integrate it in the H.265/HEVC intra encoding framework to provide a perceptually lossless image coding solution. Different to the conventional JND models, our proposed FJND model considers the relationship between contrast masking effect and the foveation properties of HVS. Furthermore, to achieving the perceptually lossless coding, the FJND model is integrated in the H.265/HEVC framework by determining the quantization parameter to ensure that the resulting distortion is no larger than the FJND threshold. The experiments demonstrate that the proposed method effectively improves the compression performance.     

A linear edge model and its application in lossless image coding

A linear edge model for the prediction of edge pixel values is first proposed and a gradient-adjusted predictor based on this model for context-based lossless image coding is then developed. Theoretical analysis and experiment results show that the performance of the proposed predictor is better than that of the state-of-the-art predictors for most test images.     

Embedding of time-varying contractive systems in lossless realizations

The lossless embedding problem, also known as the Darlington synthesis or unitary extension problem, considers the extension of a given contractive system to become the partial input-output operator of a lossless system. In this paper the embedding problem is solved for discrete-time time-varying systems with finite but possibly time-varying state dimensions, for the strictly contractive as well as the boundary case. The construction is done in a state space context and gives rise to a time-varying Riccati difference equation which is shown to have a closed-form solution. As a corollary, a discrete-time Bounded Real Lemma is formulated, linking contractiveness of an input-output operator to conditions on its state realization.This research was supported in part by the commission of the EC under the ESPRITBRA program 6632 (NANA2).