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Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion Models (2311.15908v2)

Published 27 Nov 2023 in cs.CV

Abstract: In this paper, we address the problem of enhancing perceptual quality in video super-resolution (VSR) using Diffusion Models (DMs) while ensuring temporal consistency among frames. We present StableVSR, a VSR method based on DMs that can significantly enhance the perceptual quality of upscaled videos by synthesizing realistic and temporally-consistent details. We introduce the Temporal Conditioning Module (TCM) into a pre-trained DM for single image super-resolution to turn it into a VSR method. TCM uses the novel Temporal Texture Guidance, which provides it with spatially-aligned and detail-rich texture information synthesized in adjacent frames. This guides the generative process of the current frame toward high-quality and temporally-consistent results. In addition, we introduce the novel Frame-wise Bidirectional Sampling strategy to encourage the use of information from past to future and vice-versa. This strategy improves the perceptual quality of the results and the temporal consistency across frames. We demonstrate the effectiveness of StableVSR in enhancing the perceptual quality of upscaled videos while achieving better temporal consistency compared to existing state-of-the-art methods for VSR. The project page is available at https://github.com/claudiom4sir/StableVSR.

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Authors (3)
  1. Claudio Rota (6 papers)
  2. Marco Buzzelli (9 papers)
  3. Joost van de Weijer (133 papers)
Citations (2)
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Summary

Enhancing Perceptual Quality in Video Super-Resolution with Diffusion Models

The paper presented by Claudio Rota, Marco Buzzelli, and Joost van de Weijer introduces a novel approach to Video Super-Resolution (VSR) using Diffusion Models (DMs), labeled as StableVSR. This approach is notable for its focus on enhancing perceptual quality by synthesizing realistic and temporally-consistent details, diverging from traditional methods that prioritize pixel-level reconstruction metrics such as PSNR.

Methodological Overview

The authors employ Latent Diffusion Models (LDMs) for VSR, building upon an existing pre-trained model for single-image super-resolution (SISR). The core innovation lies in utilizing the Temporal Conditioning Module (TCM), which ensures that video frames are both high-quality and temporally consistent by incorporating fine micro-scale details from adjacent frames, thus aligning with human perceptual quality metrics such as LPIPS and CLIP-IQA. An integral part of TCM is the Temporal Texture Guidance strategy, which uses spatial alignment and richness in texture from preceding video frames to inform the generative process of the current frame.

Their novel Frame-wise Bidirectional Sampling strategy addresses potential challenges like error accumulation and unidirectional biasing seen in conventional models. This technique ensures that sampling steps are undertaken across frames both forward (past to future) and backward (future to past), smoothing temporal transitions.

Implications and Findings

Quantitative analyses presented in the paper reveal that the proposed StableVSR model substantially enhances the perceptual quality over existing state-of-the-art VSR models. This is particularly evidenced by improvements in perceptual metrics such as LPIPS and CLIP-IQA, though this comes at a known trade-off—decreased performance in PSNR and SSIM, which are traditional measures of pixel-wise accuracy but not necessarily of perceived visual quality. StableVSR addresses the well-established perception-distortion trade-off in image processing, suggesting that future developments will broaden DMs' application for tasks where human-like realism is required over mere numerical reconstruction accuracy.

The framework allows leveraging the generative potential of DMs, wherein inaccuracies predicted by conventional regression-based methods do not confine the super-resolution process. The demonstrable advantage seen in synthesizing realistic high-frequency details points towards its deployment in applications requiring high-quality visual fidelity, like cinematic or sports video enhancements.

Future Directions

While offering substantial perceptual gains, the model's complexity and computational demand remain a limitation, typical of current DM implementations. As such, future work could explore optimized architectures or training paradigms that enhance efficiency without sacrificing quality, invoking advances in fast sampling methods.

Overall, this paper contributes to an evolving narrative in super-resolution research—shifting focus from pixel-wise fidelity to perceptually meaningful and contextually coherent enhancements. Thus, it encourages other researchers to continue investigating generative approaches in video processing where perceptual quality cannot be sidelined. The accompanying publicly available code repository further invites replication and extension by the community, promoting collaborative progress in the domain of AI-driven video enhancement technologies.

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