Abstract
AbstractIn this paper, we present a low complexity modified Gaussian model-based pre-processing filter to improve the performance of H.264 compressed video. Noisy video sequences captured by imaging system result in decline of coding efficiency and unpleasant coding artifacts due to higher frequency components. By incorporating local statistics and quantization parameter into filtering process, the spurious noise is significantly attenuated and coding efficiency is improved, leading to improvement of visual quality and to bit-rate saving for given quantization step size. In addition, in order to reduce the complexity of the pre-processing filter, the simplified local statistics and quantization parameter induced by analyzing H.264 transformation and quantization processes are introduced. The simulation results show the capability of the proposed algorithm.KeywordsDiscrete Cosine TransformQuantization ParameterInverse Discrete Cosine TransformVariable Length CodeVideo Code StandardThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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