Saturation based quality evaluation method for colored multi-exposure fusion image

Abstract

The invention discloses a saturation based quality evaluation method for a colored multi-exposure fusion image, and the method can be used to solve the problem that evaluation for the colored multi-exposure fusion image is inaccurate. A multi-exposure image in an MEF database and a fusion image of the multi-exposure image serve as a training sample, and texture, structure and color information is obtained from the training sample by utilizing a saturation and wavelet coefficient based extraction manner; texture, structure and color similarities are calculated respectively; all the obtained similarities, as characteristic values, together a given MOS value are input to an ELM machine learning device to implement training; and the texture, structure and color similarities of the multi-exposure fusion image and the corresponding generated fused image are input to the trained ELM machine learning device, and an evaluation result is obtained.

Description

technical field [0001] The invention belongs to the field of image quality evaluation methods, in particular to a saturation-based color multi-exposure fusion image quality evaluation method. Background technique [0002] The current ordinary display devices can display a brightness range that is far smaller than the real scene and the brightness range that the human eye can perceive, so it is impossible to display the real scene observed by the human eye on an ordinary device. The high dynamic range (High Dynamic Range, HDR) image display technology can display as much light and dark information of the real scene on common devices as possible, and conform to the real perception effect of the human eye as much as possible. With the rapid development of the high-definition digital industry, HDR display technology has become one of the current research hotspots in the digital field. Multi-exposure image fusion technology is a simple HDR display technology, which can directly ...

AI Technical Summary

Problems solved by technology

[0003] At present, the quality evaluation algorithms for fusion images are mainly divided into four categories: 1) methods based on image information, this type of method mainly uses the mutual information of images before and after fusion to evaluate the quality of the image after fusion, this type of method only considers the overall information of the image 2) Image feature-based methods, which mainly use edge features in the spatial domain and edge features in the wavelet domain to evaluate the quality of the fused image, which ignore the image 3) Based on the structure similarity method, this type of method is inspired by the SSIM algorithm, which calculates the structural

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