Transformer-Diffusion 8K Video Restoration Under Codec Compression

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Solution Overview

Problem

Existing video restoration methods struggle to efficiently remove compression artifacts in high-resolution videos, particularly 8K content, due to the significant data volume and computational demands, while maintaining high-quality visual fidelity.

Innovation Solution

A transformer diffusion model specifically designed for 8K videos, utilizing a U-shaped hierarchical network with skip connections, enhanced windowed self-attention, and auxiliary models like Look Around and Look Ahead to capture long-range dependencies and contextual information, directly addressing compression artifacts without artificial noise.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If video compression is applied to reduce data volume and computational demands, then transmission and storage efficiency improve, but compression artifacts such as blockiness, blurring, and ringing are introduced

Engineering Contradiction:

Improvedata volumeVSAvoidcompression artifacts

Core Design Contradiction:

Quantity of substanceVSObject-affected harmful factors

Solution Approach 1:

The patent applies diffusion models to convert the harmful compression artifacts into beneficial restoration effects. The model treats compression artifacts as noise that can be progressively removed through multiple diffusion steps, transforming the degraded compressed video into high-quality restored video by leveraging the denoising capabilities of diffusion processes

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

2Object-affected harmful factors

If existing video restoration methods are applied to remove compression artifacts, then visual fidelity improves, but computational complexity and processing time increase significantly for high-resolution videos

Engineering Contradiction:

Improvecompression artifactsVSAvoidcomputational complexity

Core Design Contradiction:

Object-affected harmful factorsVSDevice complexity

Solution Approach 1:

The patent segments the video restoration process into multiple manageable components: a compression artifact detection module that identifies artifact locations and types, and a diffusion-based restoration module that processes different artifact types using specialized restoration models. This segmentation allows selective processing of only artifact-affected regions rather than entire high-resolution frames, significantly reducing computational complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies partial action by focusing computational resources only on regions containing compression artifacts rather than processing the entire video frame. The artifact detection module identifies specific problematic areas, and the restoration process is applied selectively to these regions, reducing overall computational burden while maintaining restoration effectiveness

Inventive Principle:

Principle #16Partial or excessive action

3Quantity of substance

If frame resolution and color depth are reduced during encoding to improve compression efficiency, then bitrate is reduced, but video restoration quality deteriorates

Engineering Contradiction:

ImprovebitrateVSAvoidvideo restoration quality

Core Design Contradiction:

Quantity of substanceVSManufacturing precision

Solution Approach 1:

The patent applies preliminary action by performing compression artifact detection and classification before the restoration process. This preliminary analysis identifies the types and locations of artifacts introduced by compression, allowing the subsequent diffusion-based restoration to target specific problems with appropriate restoration strategies, thereby improving restoration quality from compressed low-bitrate video

Inventive Principle:

Principle #10Preliminary action

4Object-affected harmful factors

If Diffusion-based restoration processes are applied at the decoder to improve video restoration quality, then visual fidelity improves, but processing time and computational resources increase

Engineering Contradiction:

Improvecompression artifactsVSAvoidprocessing time

Core Design Contradiction:

Object-affected harmful factorsVSLoss of time

Solution Approach 1:

The patent applies local quality by using the compression artifact detection module to identify specific regions containing artifacts, then applying diffusion-based restoration only to these localized areas rather than processing entire frames. Different artifact types detected in different regions can be handled with appropriate restoration strengths and diffusion step counts, optimizing processing time while maintaining restoration quality

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20260059129A1Quantization parameter-aware transformer-diffusion approach for 8k video restoration under codec compression

Publication Date: 2026.02.26 USERFUL CORP

AI summary

A sequence of compressed video frames is received. A transformer diffusion model is applied to the sequence of compressed video frames. Applying the transformer diffusion model includes utilizing a look around model in an encoding portion of the transformer diffusion model and a look ahead model in a decoding portion of the transformer diffusion model. A restored video sequence is generated based on an output of the transformer diffusion model