| Summary: | 碩士 === 淡江大學 === 電機工程學系碩士班 === 101 === In the conventional image/video compression approach, we need to first capture the image/video signals from for example camera, and take more sampled data via sampling processes. For transmission those sampled data through various communication networks, high efficient compression algorithm is required for compressing data [2-8]. This processes of sampling analog signal and then compressing them for reducing the quantity of sampled data is a kind of wasting. Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. It has been developed from questions raised about the efficiency of the conventional signal processing pipeline for compression, coding and recovery of natural signals, including audio, still images and video. With the basic principle developed in CS, we might enable dramatically reduced measurement time, reduced sampling rates significantly, or reduced use of Analog-to-Digital converter resources.
Many natural signals have concise representations when expressed in the proper basis. Recently, for data acquisition and signal recovery based on the premise that a signal having a sparse representation in the proper basis, the technique of degradation algorithm of CS [11] was presented for image compression. It showed that the complexity as well as signal reconstruction quality could be improved significantly.
Via computer simulation, we verify that the performance is improved, in terms of the PSNR and the efficiency of the system.
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