Study On Effective Video Coding Methods Based On Random Noise Processing

Digital video signals have been playing a significant role in our everyday life, theproduction of enterprises and so on. However, because of the huge amount ofinformation that video signals contain and the extremely high band width we need totransmit them, the compression methods of digital video signals have become a hottopic in the research fields. In these years, many organizations such as InternationalStandardization Organization (ISO), International Telecommunication Union (ITU) andInternational Electro technical Commission (IEC) have developed several video codingstandards, which include H.261, H.263, H.264/AVC, MPEG-1/2/4, AVS and so on.These standards have given great contribution to the improvement of the ratio ofcompression of video signals, and also to the progress of the information industry.When concerning about the detailed techniques in these coding standards, almost all ofthese conform a predict-transform-hybrid coding framework based on pixel blocksdivision, and this framework gives a unique strategy to cope with all information invideo signals. There is no denying that this framework failed to recognize differentcomponents in video signals, which means that in most common cases, different levelof uncorrelated noise components exit in the input signals. This degrades theperformance of video encoders and causes waste of the band width when transmitting.The main contribution of this thesis is to design and explore a new video codingframework based on noise procession. In this framework, the parameters of noisecomponents are accurately estimated and noise components are filtered based on theseparameters. The parameters are encoded into the coded data stream and transmitted tothe receivers, who can decode these parameters and choose if the noise components willbe reconstructed or not. This strategy will save more band width and other resources tothe coding of the original video signals. Our works are stated below:Through the research work on the state-of-the-art video noise estimationtechniques, a noise parameter estimation algorithm based on motion estimation (ME) isproposed in Chapter3. This algorithm uses ME techniques included in video codingstandards to search for the similar spatial information and calculate the variance of theresidual. Then appropriate data are selected to calculate the average value to estimatethe accurate variance of the additive noise components.After the further study on the character of noise components contained in videosignals, another method of estimating noise variance working in transform domain is proposed in Chapter4to amend the low capability of ME based method in someparticular cases. With the assistance of the variance consistency of DCT, the variance ofthe noise components can be estimated by calculating the variance of the high frequentDCT coefficients, where most of the coefficients are acquired from noise components.After the variance of noise components is accurately estimated, a low complexitynonlocal mean video denoising method is proposed in Chapter5. The proposedalgorithm has greatly lowered the computing complexity by using block basedarchitecture. Also, some remaining problems are stated in this chapter to start a futurediscussion on the researching perspective.