Photo exposure correction is widely investigated, but fewer studies focus on correcting under- and over-exposed images simultaneously. Three issues remain open to handle and correct both under- and over-exposed images in a unified way. First, a locally adaptive exposure adjustment may be more flexible instead of learning a global mapping. Second, it is an ill-posed problem to determine the suitable exposure values locally. Third, photos with the same content but different exposures may not reach consistent adjustment results. To this end, we proposed a novel exposure correction network, ExReg, to address the challenges by formulating exposure correction as a multi-dimensional regression process. Given an input image, a compact multi-exposure generation network is introduced to generate images with different exposure conditions for multi-dimensional regression and exposure correction in the next stage. An auxiliary module is designed to predict the region-wise exposure values, guiding the proposed Encoder–Decoder ANP (Attentive Neural Process) to regress the final corrected image. The experimental results show that ExReg can generate well-exposed results and outperform the SOTA method in PSNR for extensive exposure problems. Furthermore, the processing speed, with 0.05 seconds per image on an RTX 3090, is efficient. When tested on the same image under various exposure levels, ExReg also yields results that are visually consistent and physically accurate.

The rise of deep learning has spurred advancements in image compression, with end-to-end learned systems gaining traction. However, their adoption in standard frameworks is limited, as they require a major overhaul of existing hardware designed for traditional methods. Moreover, their computational complexity, especially on the decoder side, remains significantly higher than conventional codecs. Consequently, optimizing traditional codecs remains a key research focus.

Deep-learning-based video compression technique has been rapidly growing in recent years. This paper adopts the Conditional Augmented Normalizing Flow video codec (CANF-VC) [8] as our basic system. To improve the quality of the condition signal (image) for CANF, we propose a two-layer structure learning-based video codec. At low cost of extra bit rate, the low-resolution base layer provides side information to improve the quality of motion-compensated reference frame through a super-resolution module with a merge-net. In addition, the base layer also provides information to the skip-mask generator. The skip-mask guides the coding mechanism to reduce the transmitted samples for the high-resolution enhancement layer. The experiment results indicate that the proposed two-layer coding scheme can provide 22.19% PSNR BD-Rate saving and 49.59% MS-SSIM BD-Rate saving over H.265 (HM 16.20) on the UVG test sequences.

This paper introduces a dual-critic reinforcement learning (RL) framework to address the problem of frame-level bit allocation in HEVC/H.265. The objective is to minimize the distortion of a group of pictures (GOP) under a rate constraint. Previous RL-based methods tackle such a constrained optimization problem by maximizing a single reward function that often combines a distortion and a rate reward. However, the way how these rewards are combined is usually ad hoc and may not generalize well to various coding conditions and video sequences. To overcome this issue, we adapt the deep deterministic policy gradient (DDPG) reinforcement learning algorithm for use with two critics, with one learning to predict the distortion reward and the other the rate reward. In particular, the distortion critic works to update the agent when the rate constraint is satisfied. By contrast, the rate critic makes the rate constraint a priority when the agent goes over the bit budget. Experimental results on commonly used datasets show that our method outperforms the bit allocation scheme in x265 and the single-critic baseline by a significant margin in terms of rate-distortion performance while offering fairly precise rate control.