Residual Diffusion Compression for Low-Bitrate Image Fidelity
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Solution Overview
Problem
Conventional image and video coding techniques struggle to balance the tradeoff between distortion (fidelity) and perceptual quality while compressing data, leading to artifacts in reconstructed images, and existing generative models like GANs face challenges in efficiently navigating this tradeoff during data compression.
Innovation Solution
A residual-based machine learning system using a diffusion model, such as a denoising diffusion probabilistic model (DDPM), predicts residuals over adjustable sampling steps to optimize the rate-distortion-perception tradeoff, allowing dynamic adjustment based on user input or device constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If conventional video coding techniques are used to compress video data, then the bit rate is reduced, but video quality and fidelity deteriorate with artifacts appearing in reconstructed images
Solution Approach 1:
The video coding process is segmented into two distinct stages: (1) an initial reconstruction stage using conventional codecs to produce a baseline compressed video, and (2) a residual refinement stage using a diffusion model to predict and correct errors. This segmentation allows each stage to specialize - the conventional codec handles bulk compression while the diffusion model focuses on quality enhancement, resolving the contradiction between bit rate reduction and fidelity preservation.
Solution Approach 2:
A residual diffusion model is introduced as an intermediary component between the conventional video codec and the final output. This intermediary takes the initial reconstruction and the original video as inputs, predicts the residual errors, and refines the output. The intermediary acts as a bridge that allows conventional compression techniques to operate while maintaining high fidelity through error correction, thus resolving the quality-bit rate tradeoff.
2Manufacturing precision
If generative models like GANs are used to improve perceptual quality during compression, then quality is enhanced, but computational efficiency and flexibility deteriorate
Solution Approach 1:
The system enables dynamic adjustment of the number of diffusion sampling steps as a controllable parameter. Users can configure the sampling steps based on their specific needs - more steps for higher quality when computational resources are abundant, fewer steps for faster processing when efficiency is prioritized. This parameter control resolves the contradiction by making the quality-efficiency tradeoff adjustable rather than fixed.
Solution Approach 2:
The system transitions from static, fixed-processing approaches to dynamic, adaptive processing. The number of diffusion sampling steps can be adjusted in real-time based on device constraints, user preferences, and content characteristics. This dynamic capability allows the system to optimize performance for different scenarios, resolving the contradiction between quality enhancement and computational efficiency by making both adaptable to changing conditions.
Data Source
AI summary
Systems and techniques are described for processing image data using a residual model that can be configured with an adjustable number of sampling steps. For example, a process can include obtaining a latent representation of an image and processing, using a decoder of a machine learning model, the latent representation of the image to generate an initial reconstructed image. The process can further include processing, using the residual model, the initial reconstructed image and noise data to predict a plurality of predictions of a residual over a number of sampling steps. The residual represents a difference between the image and the initial reconstructed image. The process can include obtaining, from the plurality of predictions of the residual, a final residual representing the difference between the image and the initial reconstructed image. The process can further include combining the initial reconstructed image and the residual to generate a final reconstructed image.