{"title":"Front Matter: Volume 9396","authors":"","doi":"10.1117/12.2185155","DOIUrl":"https://doi.org/10.1117/12.2185155","url":null,"abstract":"","PeriodicalId":274168,"journal":{"name":"Image Quality and System Performance","volume":"168 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114008865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"GPGPU based implementation of a high performing No Reference (NR) - IQA algorithm, BLIINDS-II","authors":"Aman Yadav, S. Sohoni, D. Chandler","doi":"10.2352/ISSN.2470-1173.2017.12.IQSP-220","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2017.12.IQSP-220","url":null,"abstract":"A relatively recent thrust in IQA research has focused on estimating the quality of a distorted image without access to the original (reference) image. Algorithms for this so-called noreference IQA (NR IQA) have made great strides over the last several years, with some NR algorithms rivaling full-reference algorithms in terms of prediction accuracy. However, there still remains a large gap in terms of runtime performance; NR algorithms remain significantly slower than FR algorithms, owing largely to their reliance on natural-scene statistics and other ensemble-based computations. To address this issue, this paper presents a GPGPU implementation, using NVidia’s CUDA platform, of the popular Blind Image Integrity Notator using DCT Statistics (BLIINDS-II) algorithm [8], a state of the art NR-IQA algorithm. We copied the image over to the GPU and performed the DCT and the statistical modeling using the GPU. These operations, for each 5x5 pixel window, are executed in parallel. We evaluated the implementation by using NVidia Visual Profiler, and we compared the implementation to a previously optimized CPU C++ implementation. By employing suitable optimizations on code, we were able to reduce the runtime for each 512x512 image from approximately 270 ms down to approximately 9 ms, which includes the time for all data transfers across PCIe bus. We discuss our unique implementation of BLIINDS-II designed specifically for use on the GPU, the insights gained from the runtime analyses, and how the GPGPU techniques developed here can be adapted for use in other NR IQA algorithms. Introduction Effective and efficient quality assessment of visual content finds application in a plenty of areas ranging from quality monitoring of video delivery systems, comparison of compression techniques to image reconstruction. Unfortunately, the benefits of recent advances in IQA and VQA have not carried over to real world systems owing largely to long execution time of these algorithms even for a single frame of image as has been pointed out in multiple publications [1][2][3][9] in the past. GPGPU based implementation for three different Full Reference IQA algorithms have been presented in [4], [5] and [6] with varying success. In time sensitive applications like quality of service monitoring in live broadcasting and video conferencing, a fast performing No Reference IQA is very essential. Addressing this strong need [7] for real time No Reference IQA, we apply GPGPU techniques to a high performing No Reference IQA algorithm, BLIINDS-II. The objective of our project is to utilize the data parallelism in BLIINDS-II NR-IQA by implementing it using a GPGPU. We aim to study the compute resources and the memory bandwidth needed along with latency issues following the data access pattern of the algorithm and propose suitable optimization techniques. Overview of BLIINDS-II algorithm BLIINDS-II is a Non Distortion Specific Natural Scene Statistics (NSS) based NR-IQA. NSS models are t","PeriodicalId":274168,"journal":{"name":"Image Quality and System Performance","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131487459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. V. Zwanenberg, S. Triantaphillidou, R. Jenkin, A. Psarrou
{"title":"Estimation of ISO12233 Edge Spatial Frequency Response from Natural Scene Derived Step-Edge Data","authors":"O. V. Zwanenberg, S. Triantaphillidou, R. Jenkin, A. Psarrou","doi":"10.2352/j.imagingsci.technol.2021.65.6.060402","DOIUrl":"https://doi.org/10.2352/j.imagingsci.technol.2021.65.6.060402","url":null,"abstract":"\u0000 The Natural Scene derived Spatial Frequency Response (NS-SFR) is a novel camera system performance measure that derives SFRs directly from images of natural scenes and processes them using ISO12233 edge-based SFR (e-SFR) algorithm. NS-SFR is a function of both camera system performance and scene content. It is measured directly from captured scenes, thus eliminating the use of test charts and strict laboratory conditions. The effective system e-SFR can be subsequently estimated from NS-SFRs using statistical analysis and a diverse dataset of scenes. This paper first presents the NS-SFR measuring framework, which locates, isolates, and verifies suitable step-edges from captures of natural scenes. It then details a process for identifying the most likely NS-SFRs for deriving the camera system e-SFR. The resulting estimates are comparable to standard e-SFRs derived from test chart inputs, making the proposed method a viable alternative to the ISO technique, with potential for real-time camera system performance measurements.\u0000","PeriodicalId":274168,"journal":{"name":"Image Quality and System Performance","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134179763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cyril Lajarge, François-Xavier Thomas, Elodie Souksava, L. Chanas, Hoang-Phi Nguyen, F. Guichard
{"title":"Objective image quality evaluation of HDR videos captured by smartphones","authors":"Cyril Lajarge, François-Xavier Thomas, Elodie Souksava, L. Chanas, Hoang-Phi Nguyen, F. Guichard","doi":"10.2352/EI.2022.34.9.IQSP-312","DOIUrl":"https://doi.org/10.2352/EI.2022.34.9.IQSP-312","url":null,"abstract":"","PeriodicalId":274168,"journal":{"name":"Image Quality and System Performance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128474295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A continuous bitstream-based blind video quality assessment using multi-layer perceptron","authors":"Hugo Merly, A. Ninassi, C. Charrier","doi":"10.2352/EI.2022.34.9.IQSP-319","DOIUrl":"https://doi.org/10.2352/EI.2022.34.9.IQSP-319","url":null,"abstract":"","PeriodicalId":274168,"journal":{"name":"Image Quality and System Performance","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114613399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multi-gene Genetic Programming based Predictive Models for Full-reference Image Quality Assessment","authors":"Naima Merzougui, L. Djerou","doi":"10.2352/j.imagingsci.technol.2021.65.6.060409","DOIUrl":"https://doi.org/10.2352/j.imagingsci.technol.2021.65.6.060409","url":null,"abstract":"\u0000 Many objective quality metrics for assessing the visual quality of images have been developed during the last decade. A simple way to fine tune the efficiency of assessment is through permutation and combination of these metrics. The goal of this fusion approach is to take advantage of the metrics utilized and minimize the influence of their drawbacks. In this paper, a symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for predicting subject scores of images in datasets using a combination of objective scores of a set of image quality metrics (IQM). By learning from image datasets, the MGGP algorithm can determine appropriate image quality metrics, from 21 metrics utilized, whose objective scores employed as predictors in the symbolic regression model, by optimizing simultaneously two competing objectives of model ‘goodness of fit’ to data and model ‘complexity’. Six large image databases (namely LIVE, CSIQ, TID2008, TID2013, IVC and MDID) that are available in public domain are used for learning and testing the predictive models, according the k-fold-cross-validation and the cross dataset strategies. The proposed approach is compared against state-of-the-art objective image quality assessment approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.\u0000","PeriodicalId":274168,"journal":{"name":"Image Quality and System Performance","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133225866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Quality-based Video Bitrate Control for WebRTC-based Teleconference Services","authors":"M. Yokota, Kazuhisa Yamagishi","doi":"10.2352/EI.2022.34.9.IQSP-333","DOIUrl":"https://doi.org/10.2352/EI.2022.34.9.IQSP-333","url":null,"abstract":"","PeriodicalId":274168,"journal":{"name":"Image Quality and System Performance","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114380746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thomas Bourbon, Coraline S. Hillairet, Benoit Pochon, F. Guichard
{"title":"New visual noise measurement on a versatile laboratory setup in HDR conditions for smartphone camera testing","authors":"Thomas Bourbon, Coraline S. Hillairet, Benoit Pochon, F. Guichard","doi":"10.2352/EI.2022.34.9.IQSP-313","DOIUrl":"https://doi.org/10.2352/EI.2022.34.9.IQSP-313","url":null,"abstract":"","PeriodicalId":274168,"journal":{"name":"Image Quality and System Performance","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121216625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exploration of comfort factors for virtual reality environments","authors":"Thibault Lacharme, M. Larabi, Daniel Méneveaux","doi":"10.2352/EI.2022.34.9.IQSP-393","DOIUrl":"https://doi.org/10.2352/EI.2022.34.9.IQSP-393","url":null,"abstract":"","PeriodicalId":274168,"journal":{"name":"Image Quality and System Performance","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116126349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Altynay Kadyrova, Marius Pedersen, Bilal Ahmad, Dipendra J. Mandal, Mathieu Nguyen, Pauline Hardeberg Zimmermann
{"title":"Image enhancement dataset for evaluation of image quality metrics","authors":"Altynay Kadyrova, Marius Pedersen, Bilal Ahmad, Dipendra J. Mandal, Mathieu Nguyen, Pauline Hardeberg Zimmermann","doi":"10.2352/EI.2022.34.9.IQSP-317","DOIUrl":"https://doi.org/10.2352/EI.2022.34.9.IQSP-317","url":null,"abstract":"","PeriodicalId":274168,"journal":{"name":"Image Quality and System Performance","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116739071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}