Abstract
In this paper, a kind of modification to the standard back propagation (SBP) algorithm has been proposed for a real-world macroblock classification problem in video coding. The proposed algorithm studies the effect of adding variance-adaptive noise to the inputs, outputs, and weight connections of multilayer feedforward neural networks during SBP training. Simulations show that the variance-adaptive weight noise is found to be more effective in improving the generalization performance of our problem. Meanwhile adaptive quantization results for moving picture experts group 2 (MPEG-2) video coding confirm the efficacy of the proposed classification method such that it can consistently produce good subjective quality and objective quality sequences at any fixed bit rate. Furthermore, due to the neural networks' fault-tolerant nature and inherent parallelism, potential exits for VLSI implementation as a small cell.
Published Version
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