一种优化的灰度图像压缩算法

An Optimized Gray-Scale Image Compression Algorithm

在保证压缩率和压缩质量的前提下提高压缩速度,是图像压缩要解决的重要问题。传统的Kamata灰度图像压缩算法具有较高的图像压缩质量,但其存在Hilbert扫描效率低及需分区后合并的不足,导致压缩效率降低。为此,设计高效灰度图像压缩算法FZF-MIP,引入高效的Hilbert解码算法,避免对具有特定前缀的输入数据进行解码,从而大幅提高Hilbert扫描效率。此外,设计高效的分区中合并策略,使得小分区的合并可在分区中统一进行,避免分区后合并的额外的时间和空间开销。实验结果表明,提出的图像压缩算法可将Kamata算法的压缩和解压效率分别提升37.8%和42.4%。

Improving compression speed while keeping compression quality and ratio is an open problem.The traditional Kamata algorithm suffers from large Hilbert scan time and partition merging operations,resulting in a low compression efficiency.To this end,this paper designs an efficient gray image compression algorithm FZF-MIP,which introduces an efficient Hilbert decoding algorithm that can avoid decoding input data with a specific prefix,thus leading to a high Hilbert scan efficiency.Besides,FZF-MIP further designs an efficient merge-in-partition strategy,which merges small partitions in the partitioning process,thus additional time and space overheadscan be avoided.Results show that FZF-MIP can increase the compression and decompression efficiency by 37.8%and 42.4%,respectively,compared with Kamata’s algorithm.