A denoising method for kmv-cast pseudo-analog video transmission based on pixel autocorrelation
An autocorrelation and analog video technology, applied in digital video signal modification, TV, color TV, etc., can solve problems such as poor effect and unable to eliminate a noise in reconstructed image
Active Publication Date: 2022-08-09
TONGJI UNIV
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[0005] On the basis of the original KMV-Cast transmission model method, the present invention uses adaptive selection to add Wiener filter as the denoising model of the receiving end, and solves the drawback that the reconstructed image cannot eliminate one noise by selectively introducing Wiener filter. A KMV-Cast pseudo-analog video transmission denoising method based on pixel autocorrelation is derived above
This method takes the transmitted pixel block as the smallest unit, based on the maximization of the signal-to-noise ratio of the pixel block, and selectively adds Wiener filtering, which solves the disadvantages of poor filtering effect of some pixel blocks. Maximize the ratio and further optimize the parameters
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[0013] The implementation process of the present invention is described in detail below, and the concrete steps are as follows:
[0014] Step 1. Image reconstruction of different similarity pixel blocks
[0015] Reconstruction specific steps of highly similar pixel blocks:
[0016] Step (11), extract cloud-related pixel blocks Among them, for each pixel block, it is assumed that there are N related pixel blocks in the cloud. The cloud-related pixel blocks are known to both the sender and the receiver. For the convenience of calculation, it is assumed that the video structure information obeys Ω~W -1 (ψ, γ) Inverse Wishart conjugate prior distribution.
[0017] Step (12), select the pixel block with the highest similarity Similarity is defined as where θ is the original image DCT transform coefficient. The video structure information of the original signal can be expressed as The value of the undetermined parameter r is determined by maximizing the subsequent signal...
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The present invention proposes a KMV-Cast pseudo-analog video transmission denoising method based on pixel autocorrelation, characterized in that the method includes: step 1, using cloud-related information to reconstruct images of highly similar pixel blocks; Image transmission, reception, reconstruction, and processing are performed on pixel blocks with low similarity; step 2, adaptive and selective addition of Wiener filtering is performed on the transmitted pixel blocks; Updates to parameter optimization. Through the above process, the reconstructed image blocks can be rationally allocated power, so that the power allocated for important pixel block transmission is higher, and the image quality is improved to the greatest extent; at the same time, the reconstructed pixel blocks can be based on the reconstructed image SNR is maximized , and adaptively choose to add Wiener filtering to maximize the quality of pixel blocks.
Description
technical field [0001] The invention relates to wireless communication and network, and is based on KMV-Cast pseudo-analog video transmission technology. Background technique [0002] In today's wireless communication, video transmission is widely used, while the traditional source-channel separation coding transmission has a cliff effect when the channel quality is poor, and the signal-to-noise ratio of the image recovered by the receiving end plummets. To solve this problem, Szymon Jakubczak and Dina Katabi proposed the SoftCast model of source-channel joint coding. Through a series of linear transformations such as 3D-DCT and Hadamard, the video quality received by the user is positively correlated with the channel quality. On the basis of SoftCast, KMV-Cast proposes a Bayesian estimation model based on maximum a posteriori probability decoding, which makes full use of the statistical information of video and cloud-related information. [0003] The KMV-Cast wireless tran...
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IPC IPC(8): H04N5/21H04N19/117H04N19/625
CPCH04N5/21H04N19/117H04N19/625Y02D30/70
Inventor 黄新林何婉宁
Owner TONGJI UNIV