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Image Quality Assessment of High Dynamic Range and Wide Color Gamut Images Maxime Rousselot

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Image Quality Assessment of High Dynamic Range and Wide Color Gamut Images Maxime Rousselot Image quality assessment of High Dynamic Range and Wide Color Gamut images Maxime Rousselot To cite this version: Maxime Rousselot. Image quality assessment of High Dynamic Range and Wide Color Gamut im- ages. Image Processing [eess.IV]. Université Rennes 1, 2019. English. NNT : 2019REN1S034. tel- 02378332 HAL Id: tel-02378332 https://tel.archives-ouvertes.fr/tel-02378332 Submitted on 25 Nov 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE DE DOCTORAT DE L’UNIVERSITE DE RENNES 1 COMUE UNIVERSITE BRETAGNE LOIRE Ecole Doctorale N°601 Mathèmatique et Sciences et Technologies de l’Information et de la Communication Spécialité: Signal, Image, Visions Par « Maxime ROUSSELOT » « Image quality assessment of High Dynamic Range and Wide Color Gamut images » «Impact of the new TV standards on the quality perception» Thèse présentée et soutenue à RENNES , le 20 Septembre 2019 Préparée à l’IRISA - Rennes et à Harmonic Inc. Rapporteurs avant soutenance : Céline LOSCOS, Professeure des universités, Université de Reims-Champagne-Ardenne Daniel MENEVEAUX, Professeur des universités, Université de Poitiers Composition du jury : Président : Eric MARCHAND, Professeur des universités, Université de Rennes 1 Examinateurs : Céline LOSCOS, Professeure des universités, Université de Reims-Champagne-Ardenne Daniel MENEVEAUX, Professeur des universités, Université de Poitiers Christophe RENAUD, Professeur des université, Université Littorale Côte d’opale Eric MARCHAND, Professeur des universités, Université de Rennes 1 Rémi COZOT, Maître de conférences, Université de Rennes 1 Olivier LE MEUR, Maître de conférences, Université de Rennes 1 Dir. de thèse : Olivier LE MEUR Maître de conférences, Université de Rennes 1 Co-dir. de thèse : Rémi COZOT Maître de conférences, Université de Rennes 1 Invité(s) Xavier DUCLOUX, Innovation Manager, Harmonic Inc. ACKNOWLEDGEMENT I would like to thank the Harmonic Innovation Team for welcoming me during these three years and and for giving me this opportunity. I would like to thank Olivier Le Meur, Rémi Cozot, Xavier Ducloux and Eric Auffret for ac- companying me throughout the thesis. Special thanks to all the participants of my experiences. Without you, my thesis would not be what it is. And finally, many thanks to my friends and family who have supported me. It would have been harder without you. TABLE OF CONTENTS Résumé en Français 8 Introduction 15 I Background and state of the art 21 Part I: Introduction 22 1 Ecosystem of HDR and WCG 23 1.1 Introduction . 23 1.2 Definitions . 23 1.2.1 Luminance and Dynamic Range . 23 1.2.2 High Dynamic Range . 24 1.2.3 Color and Gamut . 26 1.2.4 Wide Color Gamut . 27 1.3 Image representations . 28 1.3.1 Perceptually linearized luminance . 29 1.3.2 Perceptually uniform color space . 34 1.4 HDR/WCG content compression . 39 1.4.1 Pre-encoding process . 39 1.4.2 Compression: general principle . 42 1.4.3 Adaptation of codec to HDR/WCG content . 44 1.5 Conclusion . 45 2 Image quality assessment 46 2.1 Introduction . 46 2.2 Subjective methods . 46 2.3 Available HDR image databases with subjective scores . 48 2.3.1 Image description indexes . 48 2.3.2 HDR Databases presentation . 50 2.3.3 Narwaria et al. 51 2.3.4 Korshunov et al. 52 3 TABLE OF CONTENTS 2.3.5 Zerman et al. 52 2.3.6 Advantages and Drawbacks . 53 2.4 Image quality assessment: objective methods . 54 2.4.1 Adapting SDR image quality assessment . 55 2.4.2 Metrics designed for HDR content . 57 2.5 Assessing objective metric performances . 61 2.6 Conclusion . 62 Part I: Conclusion 63 II Building HDR/WCG experimental environment and analysis 65 Part II: Introduction 66 3 New databases presentation 67 3.1 Introduction . 67 3.2 Experimental protocol . 67 3.3 First database: HDdtb . 70 3.3.1 Images descriptions . 70 3.3.2 Distortion descriptions . 75 3.3.3 Subjective quality analysis . 78 3.4 Second database: 4Kdtb . 82 3.4.1 Images description . 82 3.4.2 Distortion descriptions . 87 3.4.3 Subjective quality analysis . 88 3.5 Conclusion . 90 4 Analysis of existing metrics 92 4.1 Introduction . 92 4.2 HDR-VDP-2 calibration . 92 4.2.1 Sensitivity to the screen spectral emission . 93 4.2.2 Sensitivity to the surround luminance . 96 4.2.3 Sensitivity to the angular resolution . 97 4.2.4 Conclusion on HDR-VDP-2 parameters . 99 4.3 Benchmark of existing HDR objective quality metrics . 99 4.3.1 The particular case of 4Kdtb images . 99 4.3.2 Existing metric performances . 100 4.3.3 Chrominance distortion sensitivity . 101 4 TABLE OF CONTENTS 4.4 Conclusion . 102 Part II: Conclusion 104 III New color metrics for HDR/WCG image quality assessment 105 Part III: Introduction 106 5 SDR metric adaptation 107 5.1 Introduction . 107 5.2 Proposed method . 107 5.2.1 Color Space Conversion . 108 5.2.2 Remapping Function . 108 5.2.3 Evaluation of tested SDR metrics . 110 5.3 Results . 110 5.3.1 4Kdtb Database . 110 5.3.2 Zerman et al. Database . 111 5.3.3 HDdtb Database . 112 5.3.4 Korshunov et al. Database . 113 5.3.5 Narwaria et al. Database . 113 5.3.6 Results Summary . 114 5.4 Results Discussions . 114 5.4.1 Impact of the Diffuse White Luminance . 115 5.4.2 Sensibility to Chrominance Distortions . 117 5.5 Applications . 118 5.6 Conclusion . 119 6 HDR-VDP-2 color extension 120 6.1 Introduction . 120 6.2 HDR-VDP-2 color extension . 120 6.3 Training methodology . 123 6.3.1 Defining the optimisation problem . 123 6.3.2 Training databases selection . 123 6.3.3 Combining Databases . 125 6.3.4 Validation protocol . 126 6.4 Results . 127 6.4.1 Metric performances . 127 6.4.2 Analysis of trained weights . 129 5 TABLE OF CONTENTS 6.4.3 Discussion . 131 6.5 Conclusion . 132 7 Quality metric aggregation 133 7.1 Introduction . 133 7.2 Proposed method . 133 7.2.1 Considered HDR quality metrics . 134 7.2.2 Chromatic-based visual features . 134 7.2.3 Image spatial information . 137 7.2.4 Mapping the features to the quality scores . 137 7.3 Training methodology . 137 7.3.1 Combining several databases . 137 7.3.2 Support Vector Regression . 138 7.4 Results . 139 7.4.1 Proposed metric performance . 139 7.4.2.
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