Fast macroblock mode decision for H.264/AVC baseline profile video transcoder based on support vector machines

In video transcoding, accuracy and efficiency of macroblock mode decision are critical issues at the re-encoder side due to the changes in frame size, frame rate, and bit rate. In this paper, a fast macroblock mode decision scheme based on support vector machines is proposed for H.264/AVC baseline profile video transcoder. Features including motion vectors, residual data, pre-encoded macroblock modes, and quantization parameters are extracted from incoming bitstream in both of training stage and classification stage. Feature extraction methods are investigated for spatial resolution transcoder, temporal resolution transcoder, and bit-rate transcoder. After off-line training and simplification of support vectors, the obtained support vector machine classifier can determine macroblock mode in the re-encoder accurately. Extensive experiments are carried out on different types of transcoders and results show that the proposed method can save about 80% in computational complexity compared to full mode search algorithm implemented in the latest H.264/AVC reference software (JM17.1), while maximum peak signal-to-noise ratio is degraded by 0.2–1.1 dB depending on different sequences and bit rate.