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Research on image compression algorithm based on Rectangle Segmentation and storage with sparse matrix

机译:基于矩阵分割和稀疏矩阵存储的图像压缩算法研究

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The Quarter-tree decomposition of image compression method characters with relative simplicity and fast calculation, however compression ratio is not very high. In order to overcome this flaw, one new segmentation method named the Rectangle Segmentation is proposed, in which adjacent pixel points satisfying consistency condition are viewed as the same image block. Also, without the restriction of square which abides to 2n, the image block can be rectangle which reduces the amount of block, and improves the compression ratio. Image compression ratio can be further augmented by combining the storage method of sparse matrix. Therefore, a new image compression algorithm is proposed named the Rectangle Segmentation and Sparse Matrix Storage(RSSMS) compression algorithm. Simulation results indicate that the compression ratios of images using the new algorithm is 25.19% higher than those using the Quarter-tree decomposition method.
机译:相对简单性和快速计算的图像压缩方法特征的四分之一树分解,但压缩比不是很高。为了克服该漏洞,提出了一个名为矩形分割的一个新的分段方法,其中令人满意地满足一致性条件的相邻像素点被视为相同的图像块。而且,没有遵守2N的平方的限制,图像块可以是矩形,其减少块的量,并提高压缩比。通过组合稀疏矩阵的存储方法可以进一步增强图像压缩比。因此,提出了一种名为矩形分段和稀疏矩阵存储(RSSMS)压缩算法的新图像压缩算法。仿真结果表明,使用新算法的图像的压缩比率高于使用四分之一棵树分解方法的图像的压缩比为25.19%。

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