Reversing the Damage: A QP-Aware Transformer-Diffusion Approach for 8K Video Restoration under Codec Compression

Ali Mollaahmadi Dehaghi, Reza Razavi, Mohammad Moshirpour; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1258-1267

In this paper we introduce DiQP; a novel Transformer-Diffusion model for restoring 8K video quality degraded by codec compression. To the best of our knowledge our model is the first to consider restoring the artifacts introduced by various codecs (AV1 HEVC) by Denoising Diffusion without considering additional noise. This approach allows us to model the complex non-Gaussian nature of compression artifacts effectively learning to reverse the degradation. Our architecture combines the power of Transformers to capture long-range dependencies with an enhanced windowed mechanism that preserves spatio-temporal context within groups of pixels across frames. To further enhance restoration the model incorporates auxiliary "Look Ahead" and "Look Around" modules providing both future and surrounding frame information to aid in reconstructing fine details and enhancing overall visual quality. Extensive experiments on different datasets demonstrate that our model outperforms state-of-the-art methods particularly for high-resolution videos such as 4K and 8K showcasing its effectiveness in restoring perceptually pleasing videos from highly compressed sources