An optimization of MPEG to maximize subjective quality

We present a video quality measure based on block-based features. Four block-based features (an average of FFT differences, a standard deviation of FFT differences, 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 which has been trained by subjective testing. We then introduce the quality measure into the design of an MPEG encoder to maximize the quality measure. A MPEG bit allocation algorithm is developed which is based on the quality measure that considers the bit-count and quality at the macroblock level. A simple relationship between one macroblock´s quality and overall quality is also addressed. The simulation on this optimization scheme has a higher quality measure when compared to the Test Model 5