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

机译:基于矩形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:图像压缩方法特征的四分之一树分解具有相对简单和快速计算的优点,但是压缩率不是很高。为了克服这一缺陷,提出了一种新的分割方法,称为矩形分割,其中将满足一致性条件的相邻像素点视为同一图像块。而且,在不存在正方形的限制为2n的情况下,图像块可以是矩形,其减少了块的数量,并且提高了压缩率。结合稀疏矩阵的存储方法,可以进一步提高图像压缩率。因此,提出了一种新的图像压缩算法,即矩形分割和稀疏矩阵存储(RSSMS)压缩算法。仿真结果表明,新算法的图像压缩率比四分之一树分解方法高25.19%。

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