High dynamic range (HDR) imaging technology has received increasing attention in recent years, and HDR image quality assessment (IQA) metrics are indispensable during the capturing, processing and displaying of HDR images. However, existing HDR-IQA datasets and methods neglect complex distortions during the HDR image processing schemes, leading to limited generalization performance on practical application. In this work, to facilitate the development of HDR-IQA dataset, we present HDRQAD, a large-scale HDR Quality Assessment Dataset, which possesses diversified distortions during HDR imaging technologies, abundant scenes and considerable quantity. Specifically, the HDRQAD dataset contains 1409 HDR images, which are derived from source scenes with six types of distortions during the HDR imaging schemes. In contrast to existing datasets that contain only compression artifacts, the HDRQAD includes Under-exposure, Over-exposure, Motion blur and Ghosting in HDR images achieved with multi-exposure fusion technology, conversion artifacts in HDR images achieved with single image reconstruction technology and compression artifacts during the transmission of HDR images. Furthermore, during the process of constructing the dataset, we identified three key challenges in HDR-IQA tasks: 1) dynamic range variations, 2) HDR visual artifacts with large overall gap, 3) inter-regional non-uniform image quality. Based on these observations, we propose a new end-to-end network for HDR-IQA tasks, which consists of a Distortion-aware Representation Learning (DRL) module and an Inter-Regional Quality Interaction (IRQI) module. The DRL learns the representations of dynamic range variations and HDR visual artifacts, enhancing the reliability of prior information extraction. The IRQI captures inter-regional quality dependencies with interacting and fusing intermediate distortion features for more accurately predicting image quality. Extensive experiments prove the superiority of proposed HDRQAD and demonstrate that the proposed network achieves state-of-the-art performance. The Dataset and Code will be made publicly available at HDR-IQA-Dataset.

As an important and practical way to obtain high dynamic range (HDR) video, HDR video reconstruction from sequences with alternating exposures is still less explored, mainly due to the lack of large-scale real-world datasets. Existing methods are mostly trained on synthetic datasets, which perform poorly in real scenes. In this work, to facilitate the development of real-world HDR video reconstruction, we present Real-HDRV, a large-scale real-world benchmark dataset for HDR video reconstruction, featuring various scenes, diverse motion patterns, and high-quality labels. Specifically, our dataset contains 500 LDRs-HDRs video pairs, comprising about 28,000 LDR frames and 4,000 HDR labels, covering daytime, nighttime, indoor, and outdoor scenes. To our best knowledge, our dataset is the largest real-world HDR video reconstruction dataset. Correspondingly, we propose an end-to-end network for HDR video reconstruction, where a novel two-stage strategy is designed to perform alignment sequentially. Specifically, the first stage performs global alignment with the adaptively estimated global offsets, reducing the difficulty of subsequent alignment. The second stage implicitly performs local alignment in a coarse-to-fine manner at the feature level using the adaptive separable convolution. Extensive experiments demonstrate that: (1) models trained on our dataset can achieve better performance on real scenes than those trained on synthetic datasets; (2) our method outperforms previous state-of-the-art methods. Our dataset is available at https://github.com/yungsyu99/Real-HDRV.

The high dynamic range (HDR) image recovery from the low dynamic range (LDR) image aims to estimate HDR image by decompressing luminance range and enhancing details of the LDR input. In practical usages, when faced with the over-exposed, the under-exposed or the low-light images, the state-of-art prediction methods lack the capability for ideally handling them. Aiming for this, a light adaptation HDR recovery framework (LA-HDR) is proposed, which includes the multi-images generation for adaptive details amplification in different light ranges, and the following multi-details fusion. To create the multi-images, first, the designed bit-depth enhancement network (EnhanceNet) produces the high bit-depth result with enhanced contrast. This result can be furtherly processed by user-defined denoising method to refrain the low-light noise. Meanwhile, the proposed exposure bias network (EBNet) estimates the global exposure bias of the input for rectifying the mid-range details. With the enhanced result and the exposure bias, the designed transfer functions adaptively create three multi-images containing the enhanced details in different light ranges, and they are fused by the designed multi-images fusion network (FuseNet) for the final HDR prediction. The amplification and fusion scheme ensures robust HDR recovery under different light conditions, eliminating high-light recovery artifacts from previous methods. The proposed fusion masks generation (FMG) and the global feature embedding (GFE) modules inFuseNethelp eliminate the fusion artifacts. Experimental results show that LA-HDR acquires the best average performance under various light conditions, and it receives low influence from the input light conditions among the tested state-of-art HDR recovery methods.

Compared with the conventional standard dynamic range (SDR) content, high dynamic range (HDR) content supplies viewers with more immersive experience by offering a much higher range of luminance. Most of current consumer devices cannot afford to this emerging technology, and content providers decide to create both an HDR version and an SDR version of the same video. In this letter, scalable high efficiency video coding (HEVC) scalable extension of HEVC (SHVC) serves as the coding framework where the base layer (BL) is an 8-b SDR version and the enhancement layer (EL) is a 12-b HDR version. Recently, many fast coding algorithms for SDR videos are proposed, and there is an urgent demand for fast coding algorithms for EL HDR videos. With the coding information of the BL SDR videos, this letter proposes a fast algorithm to reduce the complexity of intra coding for EL HDR videos. First, depth information of neighboring coding tree units (CTUs) in the HDR version and the colocated CTU in the SDR version is used for early coding unit (CU) depth determination. Moreover, four classifiers are trained to predict the CTU depth range. Two classifiers are trained for CTUs in frames with a high average luma, and another two classifiers are used for CTUs in frames with a low average luma. Experimental results show that the proposed algorithm achieves 43% encoding time saving on average, with only a 0.54% Bjøntegaard delta bit rate (BDBR) increase compared to the original SHVC test model.

Edges and structures are a critical part for natural images, which can help to improve the overall quality of the reconstructed image. In tasks like object removal, image inpainting or video error concealment, many related methods try to infer and recover the edge splines in the unknown regions using known splines in the neighbours as a pre-possessing procedure. In this paper, the tensor voting method is improved to pursue better inference quality of the missing edge. In the proposed method, the additional spline pairing is used to determine the most possible connectable edge spline pairs existed in the known region, which greatly overcomes the difficulty of choosing the accurate parameter for stick voting. Experiments show that the proposed method can improve the inference performance dramatically with the help of extra pairing work and acquire a better structure inference quality compared to several other inference methods.