{"title":"Image Quality Difference Perception Ability: A BIQA model effectiveness metric based on model falsification method","authors":"","doi":"10.1016/j.eswa.2024.125349","DOIUrl":null,"url":null,"abstract":"<div><div>Blind Image Quality Assessment (BIQA) methods refer to algorithms that predict image quality scores without reference images. This challenging area is crucial for preprocessing and optimizing visual tasks. Since BIQA is a small-sample task, it has become a common practice to apply transfer learning by using models pre-trained on other visual tasks, such as semantic recognition, to BIQA. This paper is interested in whether a backbone that performs better in semantic recognition also improves predictive accuracy in BIQA tasks. Comparative experiments showed that different semantic backbones with varying precision exhibit minimal differences in PLCC (Pearson Linear Correlation Coefficient) and SRCC (Spearman Rank Order Correlation Coefficient). However, further comparison shows that the semantic backbone does enhance the model’s IQA abilities, which traditional metrics fail to capture due to their oversight of quality differences between images. We term this ability as Image Quality Difference Perception Ability (IQDP Ability). Based on this, we propose an IQDP comparison method and a new metric, which can effectively compare and measure a model’s IQDP Ability, supplementing traditional metrics and providing an effective means of identifying superior models.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957417424022164","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Blind Image Quality Assessment (BIQA) methods refer to algorithms that predict image quality scores without reference images. This challenging area is crucial for preprocessing and optimizing visual tasks. Since BIQA is a small-sample task, it has become a common practice to apply transfer learning by using models pre-trained on other visual tasks, such as semantic recognition, to BIQA. This paper is interested in whether a backbone that performs better in semantic recognition also improves predictive accuracy in BIQA tasks. Comparative experiments showed that different semantic backbones with varying precision exhibit minimal differences in PLCC (Pearson Linear Correlation Coefficient) and SRCC (Spearman Rank Order Correlation Coefficient). However, further comparison shows that the semantic backbone does enhance the model’s IQA abilities, which traditional metrics fail to capture due to their oversight of quality differences between images. We term this ability as Image Quality Difference Perception Ability (IQDP Ability). Based on this, we propose an IQDP comparison method and a new metric, which can effectively compare and measure a model’s IQDP Ability, supplementing traditional metrics and providing an effective means of identifying superior models.
期刊介绍:
Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.