基于MATLAB的JPEG基本系统编码摘要和仿真结果

摘要本文介绍了基于MATLAB的JPEG基本系统编码。在图像传送过程中，经常采用JPEG格式对静止图像进行压缩编码。该编码首先将灰度图像分成8×8的像素块，然后对各个像素块进行离散余弦变换得到变换系数后再进行量化。接着，对量化后的变换系数采用Z形扫描，得到直流系数和交流系数。进而，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，根据标准的Huffman编码进行熵编码，输出压缩图像的比特序列，从而实现图像压缩。在接收端，经过Huffman解码、直流系数和交流系数可变长解码以及反量化后，再进行离散余弦逆变换后得到重建图像。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。数据压缩比在10倍左右且峰值信噪比均在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。关键词：JPEG；离散余弦变换；MATLAB；图形用户界面。AbstractThis paper introduces the JPEG basic system encoding based on MATLAB. In the process of image transmission, JPEG format is often used for compressing and encoding still images. The basic system encoding first divides the grayscale image into 8×8 pixel blocks, then performs discrete cosine transform on each pixel block to obtain the transform coefficients, which are then quantized. Subsequently, 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 using standard Huffman coding to output the bit sequence of the compressed image, thereby achieving image compression. At the receiving end, after Huffman decoding, variable-length decoding of DC coefficients and AC coefficients, and dequantization, the inverse discrete cosine transform is performed to reconstruct the image. The simulation results show that the reconstructed image is almost identical to the original image, meeting people's visual requirements. The data compression ratio is around 10 times, and the peak signal-to-noise ratio is above 30dB. Therefore, using MATLAB to implement the JPEG basic system encoding has the advantages of simplicity, speed, and minimal error, greatly improving the efficiency and accuracy of image compression.Keywords: JPEG; discrete cosine transform; MATLAB; graphical user interface.