Efficiency and efficacy are desirable properties for any evaluation metric having to do with Standard Dynamic Range (SDR) imaging or with High Dynamic Range (HDR) imaging. However, it is a daunting task to satisfy both properties simultaneously. On the one side, existing evaluation metrics like HDR-VDP 2.2 can accurately mimic the Human Visual System (HVS), but this typically comes at a very high computational cost. On the other side, computationally cheaper alternatives (e.g., PSNR, MSE, etc.) fail to capture many crucial aspects of the HVS. In this work, we present NoR-VDPNet++, a deep learning architecture for converting full-reference accurate metrics into no-reference metrics thus reducing the computational burden. We show NoR-VDPNet++ can be successfully employed in different application scenarios.

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In this tutorial, we introduce how the High Dynamic Range (HDR) imaging field has evolved in this new era where machine
learning approaches have become dominant. The main reason for this success is that the use of machine learning and deep
learning has automatized many tedious tasks achieving high-quality results overperforming classic methods. After an introduction to classic HDR imaging and its open problem, we will summarize the main approaches for merging of multiple exposures,
single image reconstructions or inverse tone mapping, tone mapping, and display visualization.

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This paper describes the ongoing activities of the Enhanced Video coding (EVC) project of the Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI). Theproject investigates how the performances of existing codecs can be improved by enhancing or replacing specific encoding tools with AI-based counterparts. The MPEG EVC codec baseline profile has been chosen as reference as it relies on encoding tools thatare at least 20 years mature yet has compression efficiency close to HEVC. A framework has been developed to interface the encoder/decoder with neural networks, independently from the specific learning toolkit, simplifying experimentation. So far, the EVCproject has investigated the intra prediction and the super resolution coding tools. The standard intra prediction modes have been integrated by a learnable predictor: experiments in standard test conditions show rate reductions for intra coded frames in excess of 4% over the reference.The use of super resolution, a state-of-the-art deep-learning approach named Densely Residual Laplacian Network (DRLN),at the decoder side have been found to provide further gains, over the reference, in the order of 3% inthe SD to HD context

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Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) is the first standards organization to develop data coding standards that have artificial intelligence (AI) as their core technology. MPAI believes that universally accessible standards for AI-based data coding can have the same positive effects on AI as standards had on digital media. Elementary components of MPAI standards–AI modules (AIMs)–expose standard interfaces for operation in a standard AI framework (AIF). As their performance may depend on the technologies used, MPAI expects that competing developers providing AIMs will promote horizontal markets of AI solutions that build on and further promote AI innovation. Finally, the MPAI framework licences (FWLs) provide guidelines to intellectual property right (IPR) holders facilitating the availability of compatible licenses to standard users.

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MPAI – Moving Picture, Audio and Data Coding by Artificial Intelligence is the first body developing data coding standards that have Artificial Intelligence (AI) as its core technology. MPAI believes that universally accessible standards for AI-based data coding can have the same positive effects on AI as standards had on digital media.
Elementary components of MPAI standards – AI Modules (AIM) – expose standard interfaces for operations in a standard AI Framework (AIF). As their performance may depend on the technologies used, MPAI expects that competing developers providing AIMs will promote horizontal markets of AI solutions that build on and further promote AI innovation.
Finally, the MPAI Framework Licenses provide guidelines to IPR holders facilitating the availability of compatible licenses to standard users.

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Efficiency and efficacy are two desirable properties of the outmost importance for any evaluation metric having to do with Standard Dynamic Range (SDR) imaging or High Dynamic Range (HDR) imaging. However, these properties are hard to achieve simultaneously. On the one side, metrics like HDR-VDP2.2 are known to mimic the human visual system (HVS) very accurately, but its high computational cost prevents its widespread use in large evaluation campaigns. On the other side, computationally cheaper alternatives like PSNR or MSE fail to capture many of the crucial aspects of the HVS. In this work, we try to get the best of the two worlds: we present NoR-VDPNet++, an improved variant of a previous deep learning-based metric for distilling HDR-VDP2.2 into a convolutional neural network (CNN). In this work, we try to get the best of the two worlds: we present NoR-VDPNet++, an improved versionof a deep learning-based metric for distilling HDR-VDP2.2 into a convolutional neural network (CNN).

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HDR-VDP 2 has convincingly shown to be a reliable metric for image quality assessment, and it is currently playing a remarkable role in the evaluation of complex image processing algorithms. However, HDR-VDP 2 is known to be computationally expensive (both in terms of time and memory) and is constrained to the availability of a ground-truth image (the so-called reference) against to which the quality of a processed imaged is quantified. These aspects impose severe limitations on the applicability of HDR-VDP 2 to realworld scenarios involving large quantities of data or requiring real-time responses. To address these issues, we propose Deep No-Reference Quality Metric (NoR-VDPNet), a deep-learning approach that learns to predict the global image quality feature (i.e., the mean-opinion-score index Q) that HDR-VDP 2 computes. NoR-VDPNet is no-reference (i.e., it operates without a ground truth reference) and its computational cost is substantially lower when compared to HDR-VDP 2 (by more than an order of magnitude). We demonstrate the performance of NoR-VDPNet in a variety of scenarios, including the optimization of parameters of a denoiser and JPEG-XT.

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Image metrics based on Human Visual System (HVS) play a remarkable role in the evaluation of complex image processing algorithms. However, mimicking the HVS is known to be complex and computationally expensive (both in terms of time and memory), and its usage is thus limited to a few applications and to small input data. All of this makes such metrics not fully attractive in real-world scenarios. To address these issues, we propose Deep Image Quality Metric (DIQM), a deep-learning approach to learn the global image quality feature (mean-opinion-score). DIQM can emulate existing visual metrics efficiently, reducing the computational costs by more than an order of magnitude with respect to existing implementations.

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