Multiple description coding and decoding method based on compressed sensing

Abstract

The invention provides a multiple description coding and decoding method based on compressed sensing, and belongs to the field of image processing. The method adaptively selects a down sampling way for images in accordance with related coefficients and sparsity, and determines multiple descriptions according to the measured values of sampled images. Descriptive information comprises currently described master information and protecting information for other descriptions. When the environment of a transmission channel is rugged and lead one description to lose, the invention can provide a method based on weighting to forecast the lost description. Meanwhile, in order to avoid waste caused by redundant information when two descriptions are received at the same time in a traditional balanced multiple description structure, the method provided by the invention can adjust the code rates of the master information and the redundant information in an optimized manner according to the conditions of channels, the coding efficiency is high, and decoded images have a higher SNR (signal to noise ratio) and an excellent visual effect.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a multi-description image encoding and decoding method based on compressed sensing. Background technique [0002] The steady development of technologies such as network, communication and pervasive computing has made real-time, high-quality image communication services possible. However, during the transmission of image and video information, the characteristics of the network, such as heterogeneity, time-varying, limited bandwidth, high bit error rate, easy packet loss, delay, and unbalanced processing capabilities at the receiving end, usually make the decoding of images difficult. The quality of the image is degraded, and sometimes it will even cause decoding errors. Real-time transmission of high-quality image information and effective control of transmission channel errors have become a challenging task. The Multiple Description Coding (MDC) mechanism is an important coding ...

AI Technical Summary

Problems solved by technology

[0003] The existing multiple description coding methods mainly include the following categories: quantization-based multiple description coding, down-sampling-based multiple Downsampling divides the original image into multiple sub-images, encodes them independently and transmits them to the decoding end through different channels. This type of method has a relatively simple encoder, but it is difficult to control redundancy and effectively protect key information. Usually It has poor performance, and generally worsens with the increase of the number of descriptions; the quantization-based coding method obtains the corresponding description by quantizing the source with different precisions, and this type of method can generally control the redundancy more flexibly. However, the design of its index allocation is difficult, and it cannot balance the coding efficiency of the edge and the central channel well; the transformation-based coding method introduces controllable correlations in the orthogonal transformation coefficients by transforming the source. The redundant information of the description is formed. At the decoding end, the lost description can be evaluated and predicted by using the received description and the redundant information in it. The prediction method usually adopts the linear interpolation method, which directly forms the transformation coefficient into different sub- Set as a description usually causes the important frequency coefficients of the image to be lost to a certain extent, resulting in a decline in image decoding quality; in addition, although the linear interpolation prediction method at the decoding end is simple, the prediction error is large, which directly affects the signal-to-noise ratio of the generated image.

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