Visual attention fusion method for redirected image quality evaluation

Abstract

The invention relates to a visual attention fusion method for redirected image quality evaluation. The method comprise: step one, an original image is read and two kinds of saliency maps are generatedby using two kinds of salient object detection algorithms; step two, equalization is carried out to reduce the distribution difference between the two saliency maps and two equalization saliency mapsare generated; step three, the equalization saliency maps are fused by using a method of adding saliency values of corresponding points and calculating an average value and then a normalization operation is performed to generate a fused saliency map; step four, face and line information in the original image is detected; step five, saliency values of a face rectangular frame and a line region inthe fused saliency map are magnified adaptively under the condition of constraining a magnification extreme value and a fused saliency map containing facial and line information is generated; and stepsix, the contrast of the fused saliency map is enhanced b using a saliency enhancement model and then normalization is performed to generate a visual attention fusion saliency map. Therefore, the consistency between objective quality assessment results and subjective perception is enhanced.

Description

technical field [0001] The invention relates to the fields of image and video processing and computer vision, in particular to a visual attention fusion method suitable for redirecting image quality assessment. Background technique [0002] The image retargeting algorithm uses a series of image transformation operations to change the size and aspect ratio of the original image to adapt to different display devices, while preserving visually important content and structure, that is, reducing content loss and structural distortion. Adapting image content to different display devices is of great significance in image display adaptation. A variety of image retargeting algorithms have been proposed, but the research on quality assessment methods for retargeted images is still a challenging task. Many existing objective quality assessment methods for redirected images calculate the loss of saliency information or use saliency-weighted image content similarity as part of the evalu...

AI Technical Summary

Problems solved by technology

These methods introduce visual saliency information, which has made a great improvement compared with the early retargeting image quality assessment methods, but because it is difficult for a single salient object detection algorithm to simulate human visual attention in retargeting image quality assessment, the objective Agreement between assessment results and subjective scores remains low

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