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Method for constructing cost function in compressed video super resolution reconstruction

A technology of super-resolution reconstruction and cost function, which is applied in the direction of digital video signal modification, television, complex mathematical operations, etc., can solve the problems of compressed video super-resolution reconstruction process modeling, the influence of coefficients before quantization, etc., to achieve high-resolution The effect of high-rate image quality, accurate cost function, and accurate quantization noise model

Inactive Publication Date: 2011-05-18
WUHAN UNIV
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Problems solved by technology

However, this method does not consider the large estimation error of the high-resolution image during the reconstruction process, which will affect the coefficients before quantization, so it is still difficult to accurately model the compressed video super-resolution reconstruction process.

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  • Method for constructing cost function in compressed video super resolution reconstruction
  • Method for constructing cost function in compressed video super resolution reconstruction
  • Method for constructing cost function in compressed video super resolution reconstruction

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Embodiment Construction

[0018] The following are the concrete steps of the embodiment of the present invention:

[0019] In order to illustrate the calculation steps, a frame is randomly taken from the test video Forman (such as figure 1 Shown) to illustrate the method of the present invention. Randomly select an 8×8 DCT transform block in the reference frame, and the specific steps are as follows:

[0020] (1) The MAP reconstruction cost function is divided into three items: A(Z, λ), B(Z, λ) and C(Z), where Z is a high-resolution image, and the parameter λ is the DCT coefficient distribution parameter before quantization; A (Z, λ) is the reconstruction error term after considering the quantization noise, B(Z, λ) is the regular constraint term formed by the distribution characteristics of the quantization coefficient, C(Z) is the general MAP reconstruction regular term, and the MAP reconstruction cost function is J(Z,λ)=A(Z,λ)+B(Z,λ)+C(Z);

[0021] (2) According to the quantization step size q and...

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Abstract

The invention discloses a method for constructing cost function by a compressed video super resolution reconstruction method; in the method, firstly, dividing MAP reconstruction cost function into three parts: a reconstruction error term, a regular constraint term containing distribution parameters of coefficient before quantification and a general constraint term; secondly, reckoning a precise quantification noise model; then, establishing a high resolution image reconstruction error item; afterwards, calculating DCT distribution parameter variance before quantification of low resolution image obtained from high resolution image degradation, minimizing the difference with the original distribution parameter variance, establishing the regular constraint term containing frequency domain coefficient distribution parameters; finally, constructing a reconstruction cost function containing double domains and double variants by the three items. The invention introduces frequency domain distribution coefficient into the calculation of the quantification noise model, so that the calculated quantification noise model is more precise, the cost function constructed based on the quantification noise degradation model is more precise, and the construction quality of compressed video supper solution is improved.

Description

technical field [0001] The invention relates to a method for constructing a cost function, in particular to a method for constructing a cost function by a compressed video super-resolution reconstruction method, which belongs to the field of video image processing. Background technique [0002] Super-resolution reconstruction technology is based on existing imaging equipment and imaging conditions, using single-frame or multi-frame discrete images with poor quality and low resolution, or multiple groups of video sequences to reconstruct images with better quality and higher resolution. High discrete image or video data. Since most of the current videos are compressed, the quantization noise generated by compression is the main reason for the degradation compared to uncompressed videos. Therefore, it is of great significance to construct a cost function that includes an accurate quantization noise model for super-resolution reconstruction of compressed video. [0003] In th...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04N7/26H04N7/50G06F17/14H04N19/126H04N19/60H04N19/85
Inventor 胡瑞敏陈萍韩镇王中元卢涛兰诚栋陈军
Owner WUHAN UNIV
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