Blind image quality evaluation method based on combining gradient signal and Laplacian of Gaussian (LOG) signal

An image quality evaluation and signal technology, applied in the field of image processing, can solve problems such as inability to obtain prediction results and difficulty

Inactive Publication Date: 2015-12-16
XI AN JIAOTONG UNIV
View PDF6 Cites 16 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the lack of original reference information, this method is also the

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Blind image quality evaluation method based on combining gradient signal and Laplacian of Gaussian (LOG) signal
  • Blind image quality evaluation method based on combining gradient signal and Laplacian of Gaussian (LOG) signal
  • Blind image quality evaluation method based on combining gradient signal and Laplacian of Gaussian (LOG) signal

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0063] The present invention is described in further detail below in conjunction with accompanying drawing:

[0064] see Figure 1 to Figure 6 , the algorithm proposed by the present invention utilizes the color information in the image, because the images involved in the following content all refer to the luminance component after the color image is converted to the YUV space. The present invention specifically comprises the following steps:

[0065] (1) Gradient modulus and LOG signal extraction and joint normalization

[0066] In the blind image quality assessment algorithm, the extraction of quality features plays a vital role. The ability of the extracted features to reflect the degree of image degradation directly affects the training of the final prediction model. Since the gradient modulus has shown very effective evaluation performance in image quality evaluation, the present invention extracts effective image quality features starting from these two primary featur...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The present invention discloses a blind image quality evaluation method based on combining a gradient signal and a Laplacian of Gaussian (LOG) signal. The method comprises: firstly extracting a gradient modulus signal and a LOG signal of an image; and normalizing the two signals by using a joint adaptive normalization process. Based on the two characteristic representations, three blind image quality evaluation methods which can predict image quality degradation are proposed. In each of the three methods, model training is performed on a public image subjective quality evaluation database by using a method that supports vector machine regression. A large number of experimental results prove that the three algorithms proposed by the invention all have good predict performance on the blind image quality evaluation. Compared with other existing algorithms, the model 3 method has the best quality evaluation performance and optimal database stability. Furthermore, the three algorithms are high in operation efficiency.

Description

【Technical field】 [0001] The invention belongs to the field of image processing, and relates to a blind image quality evaluation method, in particular to a blind image quality evaluation method combining a gradient modulus and a Laplacian of Gaussian (Laplacian of Gaussian, LOG) signal. 【Background technique】 [0002] Visual information is the most important way for human beings to obtain information about the surrounding environment. With the rapid development of digital image technology and network, image information has increasingly become an important way of information recording, transmission and storage. However, image quality degradation will inevitably be introduced in various stages of image acquisition, compression, transmission, and storage. With the popularity of various low-end image acquisition devices (such as smart phones), the degradation of image quality is even more inevitable. An algorithm that can quantitatively evaluate the subjective quality (or degr...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06T7/00
CPCG06T7/0002G06T2207/20081G06T2207/30168
Inventor 牟轩沁薛武峰
Owner XI AN JIAOTONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap