Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 143
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 994
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3134
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, many DIBR-synthesized image quality assessment (IQA) algorithms have been proposed to quantify the distortion that existed in texture images. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to accurately evaluate the visual quality of DIBR-synthesized images. To this end, this paper presents a DIBR-synthesized image quality assessment metric with Texture and Depth Information, dubbed as TDI. TDI predicts the quality of DIBR-synthesized images by jointly measuring the synthesized image's colorfulness, texture structure, and depth structure. The design principle of our TDI includes two points: (1) DIBR technologies bring color deviation to DIBR-synthesized images, and so measuring colorfulness can effectively predict the quality of DIBR-synthesized images. (2) In the hole-filling process, DIBR technologies introduce the local geometric distortion, which destroys the texture structure of DIBR-synthesized images and affects the relationship between the foreground and background of DIBR-synthesized images. Thus, we can accurately evaluate DIBR-synthesized image quality through a joint representation of texture and depth structures. Experiments show that our TDI outperforms the competing state-of-the-art algorithms in predicting the visual quality of DIBR-synthesized images.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8597928 | PMC |
http://dx.doi.org/10.3389/fnins.2021.761610 | DOI Listing |
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