Study The Traffic Video Compression Algorithm Based On Cellular Neural Network

With peace promotion of city construction, more and more urban road add thetraffic video monitoring. The developing trend of monitoring devices becomeshigh-definition, ultra-high clear, this led directly to the data quantity of videoinformation multiplication, aggravated video storage and transmission bandwidth ofnetwork load, both the increased equipment costs, and affect the video communicationof real time. Therefore, in view of the surge of the real-time hd-video data, and aneffective fast traffic video compression algorithms become indispensable and importantresearch topic.Existing traffic video compression technology is mainly according to the videointra-frame and inter-frame correlation based compression scheme. Many scholars makea lot of research on key technology and the improvement of video compression aboutthe establishment of the background on the video, the video object motion estimationand motion compensation technology and so on. But these compression algorithm havethe shortcomings, such as high complexity, hardware overhead is large or lowcompression efficiency, and so on. In order to solve this problem, this paper make fulluse of the cellular neural network hardware implementation, low complexity, suitablefor large scale integrated circuit implementation, has the advantages of parallelcomputing ability, has carried out the traffic video compression technology researchbased on cellular neural network.Through in-depth analysis of the theory and application of cellular neural network,according to the traffic video data, high real-time demand characteristic, the maininnovation points of this article is presented a traffic video compression algorithm basedon cellular neural network, which includes video preprocessing and post-processing.Main works as follows:1. In view of the situation of the existing algorithms need more frames and need alonger time, propose the background building algorithm based on cellular neuralnetwork. Removed the moving objects within the frame trough the cellular neural network, then gain the background images of no moving objects, repeated addition thebackground images, thus get the complete background image quickly.2. According to the background of the change is localized, and the existingbackground update algorithm to update the whole background, background regionupdate algorithm based on cellular neural networks is put forward. Using the ideas ofblock matching, making use of cellular neural network search, tag and replace thebackground change area, with less computational complexity completed backgroundupdate.3. Aiming at the condition of the traffic video background don’t give too muchattention, propose the algorithm of the intra-frame prediction compression based oncellular neural networks, implementation background and moving object differentcompression ratios. According to the star model design in cellular neural networksachieve movement lossless compression; keep the details of the moving targetcomposition. Design template decomposition of cellular neural network backgroundimage of low frequency information, by using difference image background obtains ahigh compression ratio.4. Design the traffic video compression based on cellular neural networksimulation system. Simulation experimental results show that under same conditions,the system in this paper relative to the h.264/AVC and mpeg-4ASP system save theaverage bit rate; Video compression ratio is not improved obviously, but save thecompression time.