Data Management

Parallel Implementation for Fast and Efficient Image Compression in Spatial Domain

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Executive Summary

The increasing availability of massively parallel computing architectures and the enormous volume of scientific data being produced make parallel data compression a natural subject of research. The authors present general-purpose Parallel algorithms for encoding and decoding using Block Truncation Coding (BTC). Their approach is to segment the image into non-overlapping blocks, which are compressed independently by the processors. They give alternative solution about how to construct, distribute and utilize the model in parallel, and study the effect on the compression performance and execution time. The work efficiency exceeds 50 % with any reasonable number of processors.

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