Distributed video coding-based adaptive correlation noise model construction system and method

Abstract

The invention discloses a distributed video coding-based adaptive correlation noise model construction system and a distributed video coding-based adaptive correlation noise model construction method, which belong to the technical field of distributed video coding. The system comprises a DC coefficient tape correlation noise (DCN) mean value calculation module, an original information-assisted residual error processing module, a judgment module and a mode establishment module. A coder transmits a DC coefficient mean value of an original Wyner-Ziv frame to a decoder; and the decoder constructsmodels of a DC coefficient tape correlation noise area and an AC coefficient tape correlation noise area respectively, and dynamically regulates the models mainly by utilizing the positioning and parameter calculation of an original information assistance model provided by the coder in combination with the motion conditions of an image so as to realize adaptive model construction. By the system and the method, the main technical problems of low accuracy and low flexibility of the prior art are solved, the correlation noise model is more accurate on the basis of not increasing the coding complexity and a transmission code rate, and the rate-distortion performance of the whole system is improved; and the system and the method can be used for a distributed video communication system.

Description

technical field [0001] The invention belongs to the technical field of video coding, and mainly relates to a distributed video coding system and a correlation noise model construction system and method, in particular to a distributed video coding adaptive correlation noise model construction system and method, which can be used in a distributed video coding system . Background technique [0002] Traditional video coding standards such as MPEG-X and H.26X series mainly rely on the encoder to use the statistical characteristics of the encoded signal to achieve compression encoding, resulting in the computational complexity of the encoder being 5 to 10 times that of the decoder. In recent years, some new video applications, such as wireless video sensor network, mobile video phone and wireless video surveillance, etc. are rapidly integrating and changing people's lives. However, these emerging multimedia applications put forward different requirements for the video codec syste...

AI Technical Summary

Problems solved by technology

This model has the following disadvantages: first, since the decoder does not have the information of the original image, the model is obtained by completely using the estimated information of the decoder, so the accuracy is limited; second, because the importance and statistical characteristics of DCN and ACN are different , the model does not treat the two differently, but uses the same model; third, because the distribution of DCN is related to the motion of the image, the model adopts a fixed distribution to simulate the distribution of DCN, and fails to dynamically calculate the DCN according to the motion of the image. Adjustment, poor flexibility

These shortcomings all affect the accuracy of CNM, resulting in a decrease in the rate-distortion performance of the entire system.

Images