基于MATLAB的JPEG图像压缩编码研究--学位论文总结

The study presented in the document "基于matlab的jpeg基本系统编码--学位论文.doc" explores the implementation of JPEG basic system encoding using MATLAB. In the process of image transmission, JPEG format is commonly used for compressing still images. The JPEG basic system encoding first divides the grayscale image into 8x8 pixel blocks, then applies Discrete Cosine Transform (DCT) to each block to obtain the transform coefficients which are then quantized. Subsequently, the quantized transform coefficients are scanned in a zigzag pattern to obtain DC coefficients and AC coefficients. The DC coefficients are encoded using predictive coding, while the AC coefficients are encoded using variable-length coding. Finally, standard Huffman coding is employed for entropy encoding to generate the compressed bitstream, thus achieving image compression. At the receiving end, after Huffman decoding, variable-length decoding of DC and AC coefficients, and dequantization, the inverse Discrete Cosine Transform (IDCT) is applied to reconstruct the image. The simulation results in MATLAB indicate that the reconstructed image closely matches the original image with nearly no discernible differences, meeting visual perception requirements. Additionally, a data compression ratio of about 10x and a peak signal-to-noise ratio exceeding 30dB are achieved. Therefore, employing MATLAB for JPEG basic system encoding offers advantages such as simplicity, speed, and minimal errors, significantly enhancing the efficiency and accuracy of image compression. The study demonstrates the effectiveness and reliability of using MATLAB for implementing JPEG basic system encoding.