We address the task of universal compressed image restoration, which involves recovering high-quality images degraded by a wide range of codecs and compression levels. While prior methods have made significant progress, they typically target specific degradation types and struggle to generalize across both traditional and learning-based codecs. To overcome this limitation, we propose a unified framework that leverages codec-aware conditioning and reinforcement learning-based fine-tuning. Specifically, we introduce a conditioning module that encodes both codec type and compression level, enabling the restoration network to adapt its behavior to diverse degradation settings. To further improve generalization, we incorporate reward-based objectives during fine-tuning, providing complementary signals that enhance training across both conventional and learned compression schemes. Experimental results demonstrate the effectiveness of our method in restoring images across a wide range of compression artifacts and scenarios.

Neural network-based in-loop filters (NNILFs) have recently been integrated into emerging video coding frameworks to enhance reconstruction quality beyond the capabilities of traditional signal processing filters. However, the high computational complexity of these filters significantly increases encoding and decoding time, hindering their real-time deployment. In this article, we propose an efficient neural in-loop filter control framework that improves decoding efficiency while preserving coding performance. Our approach introduces boundary strength-aware control to selectively skip NNILF operations on visually less critical regions. In addition, a time-distortion optimization (TDO) strategy is presented to adaptively manage the tradeoff between visual distortion and inference time during encoding. Experimental results on the NNVC reference software show that the proposed methods reduce decoding time by up to 23% and 21% for the random access (RA) and low-delay B (LDB) configurations, respectively. Apart from a minimal luma loss only in RA, chroma gains were also achieved, with Y: 0.10% U: −0.01% V: −0.14% for RA and Y: 0.00% U: −1.34% V: −0.94% for LDB. These results show that the proposed control framework offers a practical and effective solution for integrating NN-based tools into future video coding systems.

In this paper, we propose an efficient building block for neural network-based video coding (NNVC) tools, which are part of the post-VVC research. Our proposed Spatial-Channel Mixing (SCM) block explicitly separates spatial and channel mixing operations and applies them sequentially. The SCM block is integrated into existing NNLF (NN-based in-Loop Filter) and NNSR (NN-based Super Resolution) tools in NNVC by replacing their backbone blocks. Under the Common Test Conditions (CTC) of the Joint Video Experts Team (JVET) NNVC, the proposed NNLF and NNSR showed consistent BD-Rate improvements across Y, U, and V channels in various configurations, along with reductions in parameter count and MACs per pixel, demonstrating the effectiveness and efficiency of the proposed SCM block for NNVC tools.

Recently, learned image compression (LIC) has garnered increasing interest with its rapidly improving performance surpassing conventional codecs. A key ingredient of LIC is a hyperprior-based entropy model, where the underlying joint probability of the latent image features is modeled as a product of Gaussian distributions from each latent element. Since latents from the actual images are not spatially independent, autoregressive (AR) context based entropy models were proposed to handle the discrepancy between the assumed distribution and the actual distribution. Though the AR-based models have proven effective, the computational complexity is significantly increased due to the inherent sequential nature of the algorithm. In this paper, we present a novel alternative to the AR-based approach that can provide a significantly better trade-off between performance and complexity. To minimize the discrepancy, we introduce a correlation loss that forces the latents to be spatially decorrelated and better fitted to the independent probability model. Our correlation loss is proved to act as a general plug-in for the hyperprior (HP) based learned image compression methods. The performance gain from our correlation loss is ‘free’ in terms of computation complexity for both inference time and decoding time. To our knowledge, our method gives the best trade-off between the complexity and performance: combined with the Checkerboard-CM, it attains **90%** and when combined with ChARM-CM, it attains **98%** of the AR-based BD-Rate gains yet is around **50 times** and **30 times** faster than AR-based methods respectively