| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 105 === Lossless compression of encrypted images can be achieved through Slepian-Wolf (SW) coding, and the compression performance is highly related to how data dependency is exploited while decoding. In this thesis, to improve the compression performance, the statistics of current decoded subimage is estimated from the previous decoded subimages in the same resolution level and then is further refined by the decoded bit planes. Besides, an efficient approach for lossless compressing encrypted images, on the basis of the low-density parity-check accumulate (LDPCA) codes, is proposed and realized. Due to the intricate procedures, LDPCA decoding is the most time-consuming task in our scheme. As a result, a parallelized sum-product algorithm for LDPCA decoding based on CUDA is designed, and an early jump out detection mechanism is also proposed to avoid wasting computational resources on unnecessary operations. Experiment results show that the compression performance is improved about 7% in average, as compared with the state-of-the-art lossless compression scheme using SW coding, and the decoding time using parallel LDPCA decoder is about 40 times faster than the sequential LDPCA decoder.
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