| Records |
Author  |
Yasuko Sugito; Javier Vazquez; Trevor Canham; Marcelo Bertalmio |
| Title |
Image quality evaluation in professional HDR/WCG production questions the need for HDR metrics |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
| Volume |
31 |
Issue |
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Pages |
5163 - 5177 |
| Keywords |
Measurement; Image color analysis; Image coding; Production; Dynamic range; Brightness; Extraterrestrial measurements |
| Abstract |
In the quality evaluation of high dynamic range and wide color gamut (HDR/WCG) images, a number of works have concluded that native HDR metrics, such as HDR visual difference predictor (HDR-VDP), HDR video quality metric (HDR-VQM), or convolutional neural network (CNN)-based visibility metrics for HDR content, provide the best results. These metrics consider only the luminance component, but several color difference metrics have been specifically developed for, and validated with, HDR/WCG images. In this paper, we perform subjective evaluation experiments in a professional HDR/WCG production setting, under a real use case scenario. The results are quite relevant in that they show, firstly, that the performance of HDR metrics is worse than that of a classic, simple standard dynamic range (SDR) metric applied directly to the HDR content; and secondly, that the chrominance metrics specifically developed for HDR/WCG imaging have poor correlation with observer scores and are also outperformed by an SDR metric. Based on these findings, we show how a very simple framework for creating color HDR metrics, that uses only luminance SDR metrics, transfer functions, and classic color spaces, is able to consistently outperform, by a considerable margin, state-of-the-art HDR metrics on a varied set of HDR content, for both perceptual quantization (PQ) and Hybrid Log-Gamma (HLG) encoding, luminance and chroma distortions, and on different color spaces of common use. |
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600.161; 611.007 |
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Admin @ si @ SVG2022 |
Serial |
3683 |
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| Author |
Yaxing Wang; Joost Van de Weijer; Lu Yu; Shangling Jui |
| Title |
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data |
Type |
Conference Article |
| Year |
2022 |
Publication |
10th International Conference on Learning Representations |
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Conditional image synthesis is an integral part of many X2I translation systems, including image-to-image, text-to-image and audio-to-image translation systems. Training these large systems generally requires huge amounts of training data.
Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model (e.g., StyleGAN) to a conditioned synthetic image generation modules in a variety of systems. To initialize the conditional and reference branch (from a unconditional GAN) we exploit the style mixing characteristics of high-quality GANs to generate an infinite supply of style-mixed triplets to perform the knowledge distillation. Extensive experimental results in a number of image generation tasks (i.e., image-to-image, semantic segmentation-to-image, text-to-image and audio-to-image) demonstrate qualitatively and quantitatively that our method successfully transfers knowledge to the synthetic image generation modules, resulting in more realistic images than previous methods as confirmed by a significant drop in the FID. |
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Virtual |
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ICLR |
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LAMP; 600.147 |
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Admin @ si @ WWY2022 |
Serial |
3791 |
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| Author |
Yecong Wan; Yuanshuo Cheng; Miingwen Shao; Jordi Gonzalez |
| Title |
Image rain removal and illumination enhancement done in one go |
Type |
Journal Article |
| Year |
2022 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
| Volume |
252 |
Issue |
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Pages |
109244 |
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Rain removal plays an important role in the restoration of degraded images. Recently, CNN-based methods have achieved remarkable success. However, these approaches neglect that the appearance of real-world rain is often accompanied by low light conditions, which will further degrade the image quality, thereby hindering the restoration mission. Therefore, it is very indispensable to jointly remove the rain and enhance illumination for real-world rain image restoration. To this end, we proposed a novel spatially-adaptive network, dubbed SANet, which can remove the rain and enhance illumination in one go with the guidance of degradation mask. Meanwhile, to fully utilize negative samples, a contrastive loss is proposed to preserve more natural textures and consistent illumination. In addition, we present a new synthetic dataset, named DarkRain, to boost the development of rain image restoration algorithms in practical scenarios. DarkRain not only contains different degrees of rain, but also considers different lighting conditions, and more realistically simulates real-world rainfall scenarios. SANet is extensively evaluated on the proposed dataset and attains new state-of-the-art performance against other combining methods. Moreover, after a simple transformation, our SANet surpasses existing the state-of-the-art algorithms in both rain removal and low-light image enhancement. |
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Sept 2022 |
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Elsevier |
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ISE; 600.157; 600.168 |
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Admin @ si @ WCS2022 |
Serial |
3744 |
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| Author |
Zhaocheng Liu; Luis Herranz; Fei Yang; Saiping Zhang; Shuai Wan; Marta Mrak; Marc Gorriz |
| Title |
Slimmable Video Codec |
Type |
Conference Article |
| Year |
2022 |
Publication |
CVPR 2022 Workshop and Challenge on Learned Image Compression (CLIC 2022, 5th Edition) |
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Issue |
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Pages |
1742-1746 |
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Neural video compression has emerged as a novel paradigm combining trainable multilayer neural net-works and machine learning, achieving competitive rate-distortion (RD) performances, but still remaining impractical due to heavy neural architectures, with large memory and computational demands. In addition, models are usually optimized for a single RD tradeoff. Recent slimmable image codecs can dynamically adjust their model capacity to gracefully reduce the memory and computation requirements, without harming RD performance. In this paper we propose a slimmable video codec (SlimVC), by integrating a slimmable temporal entropy model in a slimmable autoencoder. Despite a significantly more complex architecture, we show that slimming remains a powerful mechanism to control rate, memory footprint, computational cost and latency, all being important requirements for practical video compression. |
| Address |
Virtual; 19 June 2022 |
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CVPRW |
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MACO; 601.379; 601.161 |
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no |
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Admin @ si @ LHY2022 |
Serial |
3687 |
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| Author |
Zhen Xu; Sergio Escalera; Adrien Pavao; Magali Richard; Wei-Wei Tu; Quanming Yao; Huan Zhao; Isabelle Guyon |
| Title |
Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform |
Type |
Journal Article |
| Year |
2022 |
Publication |
Patterns |
Abbreviated Journal |
PATTERNS |
| Volume |
3 |
Issue |
7 |
Pages |
100543 |
| Keywords |
Machine learning; data science; benchmark platform; reproducibility; competitions |
| Abstract |
Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing templates of benchmarks and supplying compute resources on demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning. |
| Address |
June 24, 2022 |
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Science Direct |
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HuPBA |
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Admin @ si @ XEP2022 |
Serial |
3764 |
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