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Noise image quality evaluation method combining reconstruction with noise scatter histograms

A technology for image quality evaluation and affine reconstruction, which is applied in graphics and image conversion, image enhancement, image analysis, etc. It can solve the problems of few noisy images without reference to objective quality evaluation methods, ignore the subjective feelings of noisy images, etc., and achieve consistency The effect of good, wide application field, good accuracy and monotonic performance

Inactive Publication Date: 2015-12-09
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

However, the common noise estimation algorithm often ignores the subjective feeling of the noise image to the observer
At present, there are few reference-free objective quality assessment methods for noisy images.

Method used

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  • Noise image quality evaluation method combining reconstruction with noise scatter histograms
  • Noise image quality evaluation method combining reconstruction with noise scatter histograms
  • Noise image quality evaluation method combining reconstruction with noise scatter histograms

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Embodiment Construction

[0044] The flow chart of this algorithm is as follows figure 1 shown.

[0045] A noise image quality evaluation method combining affine reconstruction and noise scatter histogram, comprising the following steps:

[0046] (1) Input the noise image to be evaluated as I, perform visual saliency filtering on the image I, and obtain the filtered image F:

[0047] F = I ⊗ c s f - - - ( 10 )

[0048] Among them, csf is the visual contrast sensitivity function (ContrastSensitivityFunction, CSF), which characterizes the contrast sensitivity difference of the human visual system (HumanVisualSystem, HVS) to different spatial frequency components in the image. The csf model adopted is expressed as follows:

[0049]csf(f)=2.6×(0.0192+0.114f)exp(-(0.114f) 1.1 )(11)

[0050] Among them, the spatial frequency f x and f y are th...

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Abstract

The invention discloses a noise image quality evaluation method combining reconstruction with noise scatter histograms. The method comprises the steps: 1) performing visual significance filtering for an input noise image to be evaluated; 2) performing partitioning processing of a filtering image by using a watershed image segmentation algorithm; 3) obtaining the components of a noise image signal by using a signal affine reconstruction matrix; 4) obtaining a noise residual plot through calculation; 5) calculating the noise standard deviation value of each partitioning, and performing statistics of noise scatter histograms; and 6) obtaining a noise image evaluation value through calculation. The noise image quality evaluation method combining reconstruction with noise scatter histograms utilizes a visual sensitive comparison function to perform visual substantial characteristic filtering, and utilizes the image segmentation algorithm and an affine reconstruction optimization problem to solve the components of the image signal, and can calculate the noise residual plot and perform statistics of noise scatter histograms, and can accurately obtain the noise image evaluation value.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to a noise image quality evaluation method combined with affine reconstruction and noise scatter histogram. Background technique [0002] During the process of transmission, display, and compression of digital images, the image quality is easily affected by degradation factors such as noise, blur, and block effects, which reduces the effectiveness of information. How to effectively measure the degree of image quality degradation is of great significance to the performance of image processing algorithms and the selection of optical system parameter indicators. Scientists have proposed many image quality evaluation algorithms. [0003] Among them, the subjective image quality evaluation method is the most intuitive evaluation method, but it is not suitable for practical applications due to the need to spend a lot of time and labor costs. Objective image quality eval...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T3/00
CPCG06T2207/30168G06T2207/10004G06T3/02
Inventor 赵巨峰崔光茫高秀敏
Owner HANGZHOU DIANZI UNIV
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