基于MATLAB的JPEG基本系统编码及优势分析 - 毕设论文总结

基于MATLAB的JPEG基本系统编码是一种用于对静止图像进行压缩编码的方法。本文介绍了该编码方法的实现原理和MATLAB仿真结果。首先，将灰度图像分成8×8的像素块，然后对每个像素块进行离散余弦变换，并进行量化。接着，对量化后的变换系数进行Z形扫描，得到直流系数和交流系数。然后，对直流系数采用预测编码，对交流系数采用可变长编码。最后，根据Huffman编码进行熵编码，输出压缩图像的比特序列。在接收端，经过解码和反量化后，再进行逆离散余弦变换得到重建图像。MATLAB仿真结果表明，重建图像与原始图像几乎没有任何差异，能够满足人们的视觉需求。此外，数据压缩比在10倍左右，峰值信噪比均在30dB以上。因此，采用MATLAB实现JPEG基本系统编码具有方法简单、速度快、误差小等优点，能够大大提高图像压缩的效率和精度。本文将对该编码方法的实现细节进行详细阐述，并呈现了实现该编码方法的MATLAB代码。 关键词: JPEG；离散余弦变换；MATLAB；图形用户界面 IABSTRACT The JPEG basic system coding based on MATLAB is a method for compressing and encoding still images. This paper introduces the implementation principle of this coding method and MATLAB simulation results. Firstly, the grayscale image is divided into 8×8 pixel blocks, and then each pixel block is subjected to discrete cosine transform and quantized. Then, the quantized transform coefficients are scanned in a zigzag pattern to obtain the DC coefficients and AC coefficients. Then, predictive coding is applied to the DC coefficients, and variable length coding is applied to the AC coefficients. Finally, entropy coding is performed based on Huffman coding, and the compressed image bit sequence is output. At the receiving end, after decoding and inverse quantization, the reconstructed image is obtained through inverse discrete cosine transform. MATLAB simulation results show that the reconstructed image is almost indistinguishable from the original image, meeting people's visual requirements. In addition, the data compression ratio is about 10 times, and the peak signal-to-noise ratio is above 30dB. Therefore, using MATLAB to implement JPEG basic system coding has the advantages of simple method, fast speed, and small errors, which can greatly improve the efficiency and accuracy of image compression. This paper will provide a detailed explanation of the implementation details of the coding method and present the MATLAB code for implementing this coding method. Keywords: JPEG; discrete cosine transform; MATLAB; graphical user interface