Dictionary learning-based stereo image quality evaluation method

A technology for stereo image and quality evaluation, which is applied in the field of stereo image quality evaluation based on dictionary learning, and can solve problems such as evaluating the quality of stereo images.

Active Publication Date: 2018-08-10
TIANJIN UNIV
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Problems solved by technology

There is currently no literature that combines visual saliency features with sparse dictionaries to evaluate stereoscopic image quality

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  • Dictionary learning-based stereo image quality evaluation method
  • Dictionary learning-based stereo image quality evaluation method
  • Dictionary learning-based stereo image quality evaluation method

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Embodiment Construction

[0044] In order to obtain a stereoscopic image quality evaluation method that is consistent with the subjective perception of human eyes. In this paper, we propose a stereoscopic image quality assessment method based on SIFT features and saliency sparse dictionary learning. Two dictionaries, the SIFT dictionary and the saliency dictionary, are trained using reference stereo image pairs. SIFT dictionary, perform SIFT transformation on the reference stereo image pair, use SIFT features to represent the image and use Feature-sign and Lagrangian dual method for dictionary training to obtain SIFT dictionary; significant dictionary, first combine the absolute difference map to obtain the initial stereo Visual saliency map, and the initial stereoscopic saliency map is optimized by using the central offset and fovea characteristics that conform to the human visual mechanism to obtain the final saliency map. Extract the saliency map of the reference stereo image pair, and then use the...

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Abstract

The invention belongs to the field of image processing, and relates to a stereo image quality evaluation method consistent with subjective feeling of human eyes, in particular to a dictionary learning-based stereo image quality evaluation method. An SIFT dictionary and a salient dictionary are trained by utilizing a reference stereo image pair, the SIFT dictionary specifically performs SIFT on thereference stereo image pair, images are represented with SIFT features, and dictionary training is performed by using a feature symbol method and a Lagrangian duality method to obtain the SIFT dictionary; a sparse coefficient matrix is processed to obtain corresponding distorted stereo image quality Q1 and corresponding distorted stereo image quality Q2; and finally the quality Q1 is combined with the quality Q2 to obtain a final quality score Q of a distorted stereo image pair. The method is mainly used for an image processing occasion.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to the research on the evaluation method of stereoscopic image quality, and some characteristics and sparse coding in the human visual system, and the application of sparse dictionary in the objective evaluation of stereoscopic image. Specifically, it involves a stereoscopic image quality evaluation method based on dictionary learning. Background technique [0002] Stereoscopic images / videos can bring viewers an immersive visual experience. Therefore, the generation, processing, transmission, display and quality evaluation of stereoscopic images / videos have become hot research issues in stereoscopic imaging technology. However, certain noises will inevitably be produced in each link of the stereoscopic imaging technology, causing the viewer to produce visual discomfort. It is particularly important to design a systematic and effective stereoscopic image / video quality evaluation method...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06T7/0002G06T2207/10012G06T2207/20081G06T2207/30168
Inventor 李素梅常永莉韩旭侯春萍
Owner TIANJIN UNIV
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