Researches On Image Compression Sensing Algorithm Based On Color Space

With the rapid development of science and technology, the demand for information, especially multimedia-information increases greatly. Traditional information acquisition technique follows the Nyquist Sampling Theorem, which leads to a huge number of sampling data when sampling with image or video and brings the great stress to the collection, transmission and processing of information. Compressed Sensing (CS) permits to sample and compress information simultaneously by collecting information based on the content and structure of the signal, which can reduce the volume of samples or measurements greatly.The characteristics of this kind of non-symmetrical information acquisition technique as follows:simple sampling operation and complex reconstruction process, making sampling method and sampling device simpler.Lower sampling frequency contributes to relieve network transmission pressure and storage pressure. The complicated signal reconstruction process is finished by the decoder which has powerful computing capacity. This special information acquisition structure can be used in many practical situations.The thesis is carried out from Compressed Sensing, concurrent computation and color space. Based on features of high computational complexity and overlong computing time, a novel parallel processing algorithm for CS algorithm was proposed. The multi-thread technology was used to improve the efficiency of image reconstruction algorithm. Moreover, the thesis research ways to reduce sparsity of sparse transform coefficient based on color space and interleaving extraction. A novel parallel processing algorithm for color image compressed sensing was proposed.The main research work and achievements are listed as follows:(1) Based on the research of image partition compression compressed sensing and concurrent computation, the proposed algorithm was accelerated by using OpenMP and interleaving extraction technology in multi-core processor. The efficiency and image reconstruction quality was improved greatly.(2) DCT, DWT and four kinds of reconstruction algorithm were carried out on the multi-core processor, the experiment result shows that the algorithm can reduce computational complexity, and improve image reconstruction quality by increasing the number of interleaving extraction. The number of threads increases had no impact on reconstruction quality. Furthermore, using the DWT as the sparse transform perform better compared with DCT.(3) The color image CS processing section data correlation of the three color channels has been taken into account. The algorithm converts the correlation redundancy of color channels to the statistics redundancy of image data by using color space conversion, making the sparse transform domain coefficients sparser. Interleaving extraction, as a way of sparse coefficients partition, making each sub-block coefficients shares a similar information content and computational complexity. Meanwhile, it also makes the sub-block coefficients sparser. In addition, concurrent computation was still carried for computational complexity reduction. The experiment showed that image reconstruction algorithm efficiency and image reconstruction quality were improved significantly by using liner-brightness color space for the proposed color image compressed sensing algorithm.