Support vector machine-based macro-block mode decision in MPEG-2 video compression

摘要

Compression either in videos or in images is currently headed by engineering and well-tuned heuristic approaches. Transmitting and storing raw video without compression needs more storage amount and network capability so compression is required. Many compression algorithms were proposed to solve this type of problem. In this paper, we proposed a machine learning approach for the video compression using MPEG-2 codec. The proposed mechanism is incorporated to perform an optimum macro block encoding mode decision with a reduced computational burden. Various video compression techniques encode the video frames by applying inter and intra coding scheme. Video frames are divided into macro-blocks and each macro-block is encoded either by inter or by intra coding technique. It is an important issue to decide which coding technique will be applied to compress a given macro block. To solve this problem, we applied the machine learning approach in MPEG-2 video compression. We have used support vector machine for the learning process and after learning any macro-block can be classified in intra or inter coding. Our experimental results suggest that use of machine learning in macro-block mode decision in MPEG-2 increases the PSNR while preserves the encoding and decoding time.