Research And Simulation Of Image Compression Algorithm Based On DCT

Image processing is a cross subject, which is closely related to physics, mathematics, electronic information and computer applications, including image compression, image denoising and image recognition and other theories and techniques. In the process of transmission, storage and processing of the image, the image processing and digital analysis have caused some problems because of the large size of the image and the high resolution of the image. Therefore, if the real-time requirement is higher, or the size of the storage space is concerned, we need to compress the image data first. Image compression can reduce the attribute data of image, which can reduce the complexity of time and space in the process of image processing and digital analysis. In order to achieve effective image compression, a suitable coding or transformation method is needed.In addition, in order to facilitate the image classification and recognition, image compression, transmission and recovery operations, image compression process as much as possible to reduce the distortion of the image.Image processing data throughput is very high, in order to achieve better real-time performance, more and more design using the powerful parallel FPGA to achieve. In this paper, we mainly introduce the image compression based on JPEG algorithm. JPEG based on the DCT algorithm, and made improvements to the DCT. However, after JPEG compression images will produce a problem, that is, the block effect, especially when the compression is relatively large, this block effect is particularly evident. And this paper also focuses on the how to deal with the JPEG compression produced in the process of the block effect, effectively reduce the block effect by using a k-means algorithm, and with the other removal deblocking algorithm were compared to get the better effect.In addition, MATLAB software is used to simulate the image compression, and the results are analyzed. The experimental results show that the algorithm is feasible and satisfactory results are obtained. When the compression ratio is not at the same time, the effect of the algorithm is different. When the compression ratio is relatively low, the image does not produce obvious block effect, no obvious change and then the average processing; when the compression ratio is high, the image will have a significant block effect, using the algorithm can effective removal of compressed image block effect, improve the image compression effect.