Research Of Fast Search Algorithm And Decoding Circuit System For VQ Image Compression

Vector Quantization (VQ) is an important technology in the field of image compression, which is widely used in various applications such as speech coding, audio and video compression, and teleconferencing systems. The current researches include how to cut down the computation complexity, how to reduce the average coding bit rate, how to improve the quality of reconstructed image, and which algorithm to be suitable to VLSI implementation.In this paper, firstly, based on image spatial correlation, a fast algorithm named advance predictive correlation VQ ( APCVQ) is presented, which aims at reducing the average coding time. The simulation results show that the algorithm have not only cut down the average coding time, but also reduced the average coding bit rate. Then, in order to reduce the coding time of each image vector, a fast algorithm based onMean-Order-Search is proposed. The simulation results of this algorithm show that its coding speed is twenty times faster than that of Full Search algorithm (FS), but its reconstructed image is badly ruined. For overcoming this disadvantage and keeping its advantage of short coding time, we improve the algorithm on the two aspects of structure parallelism and codebook order structure, to gain a better coding algorithm, which meets the requirement of reconstructed image and VLSI implementation. Finally, considering the advantages and disadvantages of these algorithms, a trade-off algorithm is proposed. A corresponding VLSI coding circuit system is designed and verified with FPGA. The FPGA post simulation results prove that the trade-off algorithm is an effective fast search algorithm of VQ coding on the three aspects of reducing the coding time, improving the reconstructed image quality, and lowering the difficulty of VLSI implementation.