Quality measure based approaches to MPEG encoding

In this paper, a video quality measure based on block-based features which has a high correlation with subjective test experiments is presented first. Four block-based features (the average of DFT differences, the standard deviation of DFT differences, the mean absolute deviation of wepstrum differences, and the variance of UVW differences) are extracted from the input and output video sequences and fed into a four-layer backpropagation neural network that has been trained by subjective testing. These features were selected out of a much larger set of candidate features, based on their combined ability to predict observer assessments of image quality. The quality measure is then introduced into the design of an MPEG encoder which is based on the nonconvex quality and bitcount functions at the macroblock level. The joint rate, quality, motion estimation and motion compensation algorithm is discussed. The proposed quality measure scheme has a higher, and also a more consistent, quality measure than the MPEG Test Model 5 scheme. The objective quality, as measured by the signal-to-noise ratio is also improved, and the variations in the signal-to-noise ratio and bit-rate from frame to frame are reduced. These results have also been confirmed by subsequent subjective test experiments