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Author Yasuko Sugito; Trevor Canham; Javier Vazquez; Marcelo Bertalmio edit  url
doi  openurl
  Title A Study of Objective Quality Metrics for HLG-Based HDR/WCG Image Coding Type Journal
  Year 2021 Publication SMPTE Motion Imaging Journal Abbreviated Journal SMPTE  
  Volume 130 Issue 4 Pages 53 - 65  
  Keywords  
  Abstract In this work, we study the suitability of high dynamic range, wide color gamut (HDR/WCG) objective quality metrics to assess the perceived deterioration of compressed images encoded using the hybrid log-gamma (HLG) method, which is the standard for HDR television. Several image quality metrics have been developed to deal specifically with HDR content, although in previous work we showed that the best results (i.e., better matches to the opinion of human expert observers) are obtained by an HDR metric that consists simply in applying a given standard dynamic range metric, called visual information fidelity (VIF), directly to HLG-encoded images. However, all these HDR metrics ignore the chroma components for their calculations, that is, they consider only the luminance channel. For this reason, in the current work, we conduct subjective evaluation experiments in a professional setting using compressed HDR/WCG images encoded with HLG and analyze the ability of the best HDR metric to detect perceivable distortions in the chroma components, as well as the suitability of popular color metrics (including ΔITPR , which supports parameters for HLG) to correlate with the opinion scores. Our first contribution is to show that there is a need to consider the chroma components in HDR metrics, as there are color distortions that subjects perceive but that the best HDR metric fails to detect. Our second contribution is the surprising result that VIF, which utilizes only the luminance channel, correlates much better with the subjective evaluation scores than the metrics investigated that do consider the color components.  
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  Notes CIC Approved no  
  Call Number SCV2021 Serial 3671  
Permanent link to this record
 
 
Author Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell edit   pdf
url  openurl
  Title Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects Type Journal Article
  Year 2020 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A  
  Volume 37 Issue 1 Pages 1-15  
  Keywords  
  Abstract Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results.  
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  Area Expedition Conference  
  Notes CIC; 600.140; 600.12; 600.118 Approved no  
  Call Number Admin @ si @ SBV2019 Serial 3311  
Permanent link to this record
 
 
Author Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez edit  url
openurl 
  Title Enhancing spatio-chromatic representation with more-than-three color coding for image description Type Journal Article
  Year 2017 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A  
  Volume 34 Issue 5 Pages 827-837  
  Keywords  
  Abstract Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding.  
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  Area Expedition Conference  
  Notes CIC; 600.087 Approved no  
  Call Number Admin @ si @ RVB2017 Serial 2892  
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Author Jaykishan Patel; Alban Flachot; Javier Vazquez; David H. Brainard; Thomas S. A. Wallis; Marcus A. Brubaker; Richard F. Murray edit  url
openurl 
  Title A deep convolutional neural network trained to infer surface reflectance is deceived by mid-level lightness illusions Type Journal Article
  Year 2023 Publication Journal of Vision Abbreviated Journal JV  
  Volume 23 Issue 9 Pages 4817-4817  
  Keywords  
  Abstract A long-standing view is that lightness illusions are by-products of strategies employed by the visual system to stabilize its perceptual representation of surface reflectance against changes in illumination. Computationally, one such strategy is to infer reflectance from the retinal image, and to base the lightness percept on this inference. CNNs trained to infer reflectance from images have proven successful at solving this problem under limited conditions. To evaluate whether these CNNs provide suitable starting points for computational models of human lightness perception, we tested a state-of-the-art CNN on several lightness illusions, and compared its behaviour to prior measurements of human performance. We trained a CNN (Yu & Smith, 2019) to infer reflectance from luminance images. The network had a 30-layer hourglass architecture with skip connections. We trained the network via supervised learning on 100K images, rendered in Blender, each showing randomly placed geometric objects (surfaces, cubes, tori, etc.), with random Lambertian reflectance patterns (solid, Voronoi, or low-pass noise), under randomized point+ambient lighting. The renderer also provided the ground-truth reflectance images required for training. After training, we applied the network to several visual illusions. These included the argyle, Koffka-Adelson, snake, White’s, checkerboard assimilation, and simultaneous contrast illusions, along with their controls where appropriate. The CNN correctly predicted larger illusions in the argyle, Koffka-Adelson, and snake images than in their controls. It also correctly predicted an assimilation effect in White's illusion. It did not, however, account for the checkerboard assimilation or simultaneous contrast effects. These results are consistent with the view that at least some lightness phenomena are by-products of a rational approach to inferring stable representations of physical properties from intrinsically ambiguous retinal images. Furthermore, they suggest that CNN models may be a promising starting point for new models of human lightness perception.  
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  Notes MACO; CIC Approved no  
  Call Number Admin @ si @ PFV2023 Serial 3890  
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Author Danna Xue; Javier Vazquez; Luis Herranz; Yang Zhang; Michael S Brown edit  url
openurl 
  Title Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring Type Journal Article
  Year 2023 Publication Computer Graphics Forum Abbreviated Journal CGF  
  Volume Issue Pages  
  Keywords  
  Abstract Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette-based color consistency method and incorporates three high-level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi-consistency workflow and showcase the user-friendly nature of our framework.  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes CIC; MACO Approved no  
  Call Number Admin @ si @ XVH2023 Serial 3883  
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