Study On The Blocking Effect Reduction Of Digital Images

With the rapid development of the digital multimedia technology, there is a variety of growing demand for the digital image. Since the digital image has the characteristics of a great amount of information, it is required to be compressed. At present, many international digital image coding standards use the block-based DCT transform coding techniques, which efficiently compressed the digital image. However, this technique will cause blocking effect at the decoded digital image, which badly reduces the subjective quality of the image and video. Therefore, there is an urgent need for efficient de-blocking algorithm.In this paper, in order to improve the subjective image quality, we carefully researched the de-blocking algorithm and proposed a classification based de-blocking algorithm, which efficiently remove the blocking effect and improve the subjective quality of the image. Besides, we proposed a time-domain correlation based blocking detection algorithm, which could efficiently detect blocking effects in the video images.This paper studied the following aspects:Generally described the basic theory and trends of the de-blocking algorithm, and analyzed the current existing technology on de-blocking algorithm.Based on the analysis of the existing de-blocking algorithm, we proposed a classification based de-blocking algorithm: firstly an image is divided into different regions according to the characteristics of the DCT coefficients of the image. Then the blocking effect is smoothed with varying degrees of intensity according to the image region which the blocking effect belongs to. Finally, it is proved by simulation that the algorithm can efficiently remove the blocking effect while protecting image details.Based on the characteristics that during image processing, the blocking effect will randomly appear at the boundary of some macro block of some frames in the video sequence, we proposed time-domain correlation based blocking detection algorithm, which use the relevance of pixels in the current frame and previous frame or subsequent frame. We showed by simulation that the algorithm can effectively detect the blocking effect of video sequences.