Noise Calibration: Plug-and-play Content-Preserving Video Enhancement using Pre-trained Video Diffusion Models (2407.10285v1)

Abstract: In order to improve the quality of synthesized videos, currently, one predominant method involves retraining an expert diffusion model and then implementing a noising-denoising process for refinement. Despite the significant training costs, maintaining consistency of content between the original and enhanced videos remains a major challenge. To tackle this challenge, we propose a novel formulation that considers both visual quality and consistency of content. Consistency of content is ensured by a proposed loss function that maintains the structure of the input, while visual quality is improved by utilizing the denoising process of pretrained diffusion models. To address the formulated optimization problem, we have developed a plug-and-play noise optimization strategy, referred to as Noise Calibration. By refining the initial random noise through a few iterations, the content of original video can be largely preserved, and the enhancement effect demonstrates a notable improvement. Extensive experiments have demonstrated the effectiveness of the proposed method.

• The paper introduces a novel Noise Calibration technique that refines initial noise to improve video quality without compromising content.
• It integrates diffusion model denoising with content consistency, significantly reducing content loss compared to standard methods.
• Extensive experiments demonstrate marked improvements in metrics like MSE, SSIM, DOVER, CLIP-IQA, and spatial frequency.

Noise Calibration: Plug-and-Play Content-Preserving Video Enhancement using Pre-trained Video Diffusion Models

Introduction

In the field of generative models, diffusion models have emerged as a powerful tool, outperforming traditional Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in various applications, particularly in visual synthesis. However, enhancing generated videos while preserving the original content remains a significant technical challenge. This paper introduces a novel framework that combines visual quality improvement with content consistency, utilizing a newly proposed plug-and-play noise optimization strategy termed Noise Calibration.

Core Contributions

The paper makes several critical contributions to the field of video enhancement using diffusion models:

1. Novel Formulation for Video Enhancement: The paper presents a new formulation that integrates visual quality enhancement with content consistency. This formulation ensures that the enhanced video remains faithful to the original content while benefiting from the high visual quality generated by diffusion models.
2. Noise Calibration Strategy: The authors introduce Noise Calibration, an optimization technique that refines initial random noise through a few iterations. This strategy drastically reduces the content loss between the original and enhanced video, ensuring that the structural integrity of the original video is maintained.
3. Extensive Empirical Evaluation: The method's efficacy is thoroughly validated through multiple quantitative and qualitative experiments. The results demonstrate significant improvements in all evaluation metrics, including metrics that measure both content consistency and visual quality.

Methodology

Diffusion Model Preliminaries

Diffusion models generate images by starting with a random noise sample and iteratively denoising it. For a given video frame $x_t$ at timestep $t$, the model predicts the next step $x_{t-1}$ using a neural network trained to estimate the noise at each step.

SDEdit Background

SDEdit is a stochastic differential equation-based technique that initializes the denoising process at an intermediate step using a reference image, balancing between realism and fidelity.

Proposed Approach: Noise Calibration

The Noise Calibration strategy is designed to minimize the content loss function, ensuring that the enhanced video maintains its original structure. This is achieved by iteratively refining the initial random noise, which is then added to the reference video to enhance its quality. The process involves low-frequency component adjustments to maintain content consistency while allowing for effective visual quality enhancements.

Experimental Validation

Quantitative Analysis

The effectiveness of the proposed method is validated quantitatively using multiple metrics:

• Consistency Metrics: MSE and SSIM scores between the enhanced and original video.
• Visual Quality Metrics: DOVER and CLIP-IQA scores, which assess the visual quality of the generated video.
• Structural Metrics: Spatial Frequency (SF), which measures the detail and texture of the video frame.

The proposed method significantly outperforms baseline approaches across all metrics, requiring minimal additional computational resources during inference.

Qualitative Evaluation

Visual comparisons highlight the method's ability to enhance video quality while maintaining content integrity. Existing methods often introduce artifacts or alter existing details, which the proposed method effectively mitigates.

Ablation Studies

The paper also provides a detailed ablation paper, examining the impact of various parameters such as the number of iteration steps and the threshold frequency. These studies confirm the robustness and adaptability of the proposed method, demonstrating its applicability to a wide range of tasks beyond video enhancement.

Broader Impacts

The proposed method has significant implications for both practical and theoretical advancements in video enhancement. By providing a training-free, plug-and-play solution, it simplifies the process of video quality enhancement, making it more accessible for various applications.

Conclusion

This paper introduces a novel approach to video enhancement, which balances quality improvement with content preservation using pre-trained diffusion models. The proposed Noise Calibration technique effectively addresses the challenge of maintaining content consistency while enhancing visual quality. The extensive experiments and analyses validate the robustness and effectiveness of this method, marking a significant contribution to the field of generative models and video enhancement. Future work could explore more sophisticated consistency objectives to further optimize this approach.