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Objective colorful image quality evaluation method based on online manifold learning

A technology for objective quality evaluation and color image, applied in image enhancement, image analysis, image data processing and other directions, can solve problems such as a large amount of motion overhead

Active Publication Date: 2016-08-31
NINGBO UNIV
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Problems solved by technology

However, this kind of image quality objective evaluation method based on sparse representation needs to use the orthogonal matching pursuit algorithm for sparse coding, which requires a lot of motion overhead. Moreover, the acquisition of the over-complete dictionary of this kind of method is completed by offline operation, which requires a lot of effective natural images as training samples, and has limitations for image processing with real-time requirements

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  • Objective colorful image quality evaluation method based on online manifold learning
  • Objective colorful image quality evaluation method based on online manifold learning
  • Objective colorful image quality evaluation method based on online manifold learning

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Embodiment Construction

[0035] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0036] A color image quality objective evaluation method based on online manifold learning proposed by the present invention, its overall realization block diagram is as follows figure 1 As shown, it includes the following steps:

[0037] ① Order I R Denotes an undistorted reference image with width W and height H, let I D means with I R The corresponding distorted image to be evaluated.

[0038] ② Using the existing visual saliency detection algorithm (Saliency Detection based on SimplePriors, SDSP), obtain the I R and I D The respective saliency maps are denoted as M R and M D ; then according to M R and M D , to calculate the maximum fused saliency map, denoted as M F , the M F The pixel value of the pixel point whose coordinate position is (x, y) is recorded as M F (x,y), M F (x,y)=max(M R(x,y), M D (x,y)), among them, 1≤x≤W...

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Abstract

The invention discloses an objective colorful image quality evaluation method based on online manifold learning. According to the method, taking consideration of the relationship between significance and objective image quality evaluation, a visual significance detection algorithm is utilized, the largest fusion significance graph is acquired through solving a respective significance graph of a reference image and a distortion image, a significance difference value of a reference image block and a corresponding distortion image block can be balanced on the basis of the largest significance of an image block of the largest fusion significance graph by utilizing an absolute difference value, so a reference visual significance image block and a distortion visual significance image block are extracted through screening, an objective quality evaluation value of the distortion image can be calculated by utilizing manifold characteristic vectors of the reference visual significance image block and the distortion visual significance image block, the evaluation effect is substantially improved, and pertinence between an objective evaluation result and subjective perception is high.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to an objective evaluation method of color image quality based on online manifold learning. Background technique [0002] Limited by the performance of the image processing system, various types of distortion will be introduced in the process of image acquisition, transmission, and encoding. The introduction of distortion will reduce the quality of the image and hinder people from obtaining information from the image. Image quality is an important index to compare the performance of various image processing algorithms and the parameters of image processing systems. Therefore, it is of great value to construct effective image quality evaluation methods in the fields of image transmission, multimedia network communication, and video analysis. Generally, image quality evaluation methods are divided into two categories: subjective evaluation and objective evaluation. Since the final...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06V10/50G06V10/56
CPCG06T7/0002G06T2207/30168G06T2207/10024G06T2207/20081G06T2207/20021G06V40/172G06V10/993G06V10/50G06V10/513G06V10/56G06V10/763G06V10/7715G06F18/23213G06F18/22G06F18/2135
Inventor 蒋刚毅何美伶陈芬宋洋
Owner NINGBO UNIV
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