基于感知器的快速彩色图像无损压缩算法

针对彩色图像像素和颜色分量之间存在的相关性，将神经网络中的感知器技术引入彩色图像的无损压缩算法中，提出了一种新的预测模型。应用感知器的自学习和自适应能力，对预测值进行自适应调整，使算法在运行过程中具有很小的预测残差并具有较小的动态范围。同时应用颜色空间变换来减小颜色分量间的相关性。相对于新的国际标准JPEG—LS，这种预测模型具有较低的计算复杂性。对比实验结果说明，这种算法的性能明显优于传统算法，在压缩比损失很小的前提下，其执行速度高于JPEG—LS。     

一种将DVD高清素材应用到PPT中的无损转换方法

文中给出了一种DVD高清素材到PPT的无损转换方法,该方法具有转换速度快、素材清晰度高、PPT播放方式灵活等优点。     

一种JPEG无损压缩算法的仿真与实现

研究图像压缩算法优化算法的效果,针对目前通用无损压缩算法都难以对JPEG文件进行压缩,提出了一种洗牌算法和无损压缩算法相结合的无损压缩方法。在将JPEG图像文件进行压缩前对文件进行有效规整,产生冗余,然后再结合通用无损压缩算法进行无损压缩进一步去除文件内部信息冗余。进行仿真实验验证,算法能够进一步将JPEG图像文件无损压缩1%-3%。结果表明,上述算法能进一步无损去除文件内部冗余,减小文件体积,算法简单,时间复杂度低,易于实现,是一种有效的快速图像压缩算法。算法已经获得专利保护。     

干涉超光谱图像的压缩算法

干涉超光谱图像的数据呈现非平稳特性,相邻谱线之间的相关性较弱,所以直接采用小波变换不利于去除数据之间的相关性.提出了一种将光谱数据采用EMD预处理,并对处理后的数据采用分类处理的算法,将每根谱线分为主干涉区域和非主干涉区域两部分,分别采用相似匹配和二次曲线拟合的方法对数据进行分解,避免了相似匹配中不利于硬件实现的开方运算.仿真结果表明:该算法可降低无损压缩输出码率达0.3bpp左右,并且可以提高有损压缩的效率.     

预测与可变长编码在图像压缩中的应用

图像压缩成为了多媒体技术的关键和瓶颈.预测编码(DPCM)和可变长编码(VLC)是两种最重要的无损压缩算法.介绍预测编码的原理,证明了对DPCM数据进行可变长编码的可行性,并对标准测试图像进行测试得到了测试结果,最后对结果进行了分析.     

A module-based LSB substitution method with lossless secret data compression

All the various data hiding methods can be simply divided into two types: (1) the extracted important data are lossy, (2) the extracted important data are lossless. The proposed method belongs to the second type. In this paper, a module-based substitution method with lossless secret data compression function is used for concealing smoother area of secret image by modifying fewer pixels in the generated stego-image. Compared with the previous data hiding methods that extract lossless data, the generated stego-image by the proposed method is always with better quality, unless the hidden image is with very strong randomness.     

An improved lossless data hiding scheme based on image VQ-index residual value coding

Copyright protection and information security have become serious problems due to the ever growing amount of digital data over the Internet. Reversible data hiding is a special type of data hiding technique that guarantees not only the secret data but also the cover media can be reconstructed without any distortion. Traditional schemes are based on spatial, discrete cosine transformation (DCT) and discrete wavelet transformation (DWT) domains. Recently, some vector quantization (VQ) based reversible data hiding schemes have been proposed. This paper proposes an improved reversible data hiding scheme based on VQ-index residual value coding. Experimental results show that our scheme outperforms two recently proposed schemes, namely side-match vector quantization (SMVQ)-based data hiding and modified fast correlation vector quantization (MFCVQ)-based data hiding.     

Identification and data-driven model reduction of state-space representations of lossless and dissipative systems from noise-free data

We illustrate procedures to identify a state-space representation of a lossless or dissipative system from a given noise-free trajectory; important special cases are passive systems and bounded-real systems. Computing a rank-revealing factorization of a Gramian-like matrix constructed from the data, a state sequence can be obtained; the state-space equations are then computed by solving a system of linear equations. This idea is also applied to perform model reduction by obtaining a balanced realization directly from data and truncating it to obtain a reduced-order model.     

Investigation of block-sorting of multiset permutations

A recent development in data compression area is Burrows–Wheeler Compression algorithm (BWCA). Introduced by Burrows and Wheeler, the BWCA achieves compression ratio closer to the best compression techniques, such as partial pattern matching (PPM) techniques, but with a faster execution speed. In this paper, we analyze the combinatorial properties of the Burrows–Wheeler transformation (BWT), which is a block-sorting transformation and an essential part of the BWCA, introduce a new transformation, and delineate the new transformation with the BWT based on the multiset permutations.