Full-reference image quality evaluation method based on image saliency detection
An image quality evaluation and image quality technology, applied in image enhancement, image analysis, image data processing and other directions, can solve the problems of low operability, high cost, slow speed, etc.
Active Publication Date: 2019-08-02
XIDIAN UNIV
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The "Mean Opinion Score" (MOS, Mean Opinion Score), which uses a large number of observers to score and take the average, has been the optimal strategy for evaluating digital images for a long time, but some defects of this method cannot be ignored, because The observation motivation, knowledge background, observation environment and psychological state of the observers are all different, so it is impossible to make accurate and repeatable evaluations. Secondly, this method needs to spend a lot of manpower and material resources, and the speed is slow and the cost is too high. Not strong, and this method cannot be described by a mathematical model, it is difficult to be widely used in industrial production
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[0080] Objective image quality evaluation methods can be divided into: full reference, no reference and partial reference image quality evaluation methods. The invention belongs to a full-reference image quality evaluation method.
[0081] Robust visual saliency enables proper processing of images without prior knowledge. Through the study, it was thought that human cortical cells might be hard-coded in their receptive fields to preferentially respond to high-contrast stimuli. Detection methods based on global contrast tend to separate large-scale objects from their surroundings. This method outperforms those local contrast methods that usually yield higher saliency only near contours. Global considerations can assign similar saliency values to similar regions in an image, and can evenly highlight objects. The salience of a region is mainly determined by its contrast with the surrounding regions, and regions that are far away play a smaller role.
[0082] The present inv...
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The invention discloses a full reference image quality evaluation method based on image saliency detection. The method comprises the following steps of: performing spatial conversion (extracting a distorted image and a corresponding original image, and converting the image from an RGB color space to a Lab color space); image segmentation (segmenting a color image in the Lab color space into regions), RCC saliency detection, super-pixel segmentation (carrying out super-pixel segmentation on a distorted image in an RGB color space and an original image corresponding to the distorted image), AMCsignificance detection and VSI calculation. The beneficial effects of the invention are that: (1) a saliency region of an image is obtained through saliency detection, image quality evaluation is carried out by applying the extracted saliency image, and the problem that the human eye vision system is difficult to model is avoided, so that the evaluation method provided by the invention is simple in idea, better conforms to the characteristics of the human eye vision system, and has better consistency with subjective evaluation; and (2) the method has relatively high robustness and relatively good prediction performance.
Description
technical field [0001] The invention relates to an image quality evaluation method, in particular to a full-reference image quality evaluation method based on image saliency detection, and belongs to the technical field of digital video image quality evaluation. Background technique [0002] With the continuous development of computer technology, human's demand for image processing has increased significantly, and it has been widely used in remote sensing, biomedicine, military, industrial and agricultural production, government work and other fields. Biopsychological studies have proved that for an image, humans only pay attention to the very few salient parts and ignore other areas. Image saliency detection can only focus on the salient area of an image and discard other parts, which greatly saves computing time and memory for image processing, so image saliency detection plays a very important role in image processing . [0003] Given that most digital image processin...
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IPC IPC(8): G06T7/00G06T7/11G06T7/90
CPCG06T7/0002G06T7/11G06T7/90G06T2207/30168
Inventor 陈晨
Owner XIDIAN UNIV