The conversion from SDR contents to HDR version, termed inverse Tone Mapping (iTM), is substantially a non- linear mapping problem. The neural network provides the potential of learning this kind of non-linear mapping in an end-to-end way. This paper proposes a Generative Adversarial Network (GAN) with an aim to reconstruct an HDR image from a single-exposure. Unlike previous work that adopts a U-net as generator, the proposed GAN structures the generator using three branches to extract the global level, regional level, and local level details of an image for further fusion. Our discriminator adopts a slim architecture, which successfully solves the conventional color excursion problem at a low cost. Moreover, to train the proposed GAN effectively, we design a mixed loss function where the pixel-wise color is incorporated. Experimental results demonstrate that the proposed GAN scheme achieves state-of-the-art performance.
Convolutional Neural Network (CNN) has been introduced to in-loop filtering in video coding for further performance improvement. For intra frame coding, a CNN model can be directly trained through learning the correlation between the reconstructed and the original frames, and the obtained model can then be applied to every single reconstructed frame to help improve the video quality. In contrast, for inter frame coding, intertwined reference dependency exists across frames. If a similar procedure of model training and deployment is adopted for inter as that for intra coding, over-smoothed reconstructed frames may be generated, which may further seriously deteriorate the overall coding performance. To address such an issue, state-of-the-art work resorts to the Rate Distortion Optimization (RDO) to determine whether to adopt the conventional or the CNN-based scheme for in-loop filtering, however leading to high computational complexity. In this paper, we propose a Coding-unit (CU) level Adaptive Decision approach (CAD) which employs an early decision for each CU, based on their coding parameters. Experimental results show that the proposed scheme achieves comparable performance with that of the RDO scheme while effectively reduces the encoding time complexity.