Video Artifact Assessment Using Texture Segmentation

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Solution Overview

Problem

Existing video delivery systems face challenges in identifying and mitigating artifacts such as banding artifacts, which are difficult to detect and often occur in regions with varying texture complexity, leading to inefficiencies in resource allocation and assessment accuracy.

Innovation Solution

A system that utilizes texture information to focus artifact assessment on regions prone to perceptible artifacts by segmenting images into different regions based on texture characteristics, using neural networks to analyze and classify these regions, and applying masking or weighting techniques to improve artifact detection and mitigation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If artifact assessment is performed on entire video content, then comprehensive coverage is achieved, but computational resources are wasted on regions where artifacts are not perceptible

Engineering Contradiction:

Improveartifact assessment accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The video content is segmented into multiple regions based on texture characteristics. Each region is then assessed separately for artifacts. This segmentation allows the system to focus computational resources only on regions where artifacts are likely to be perceptible, rather than processing the entire video content uniformly.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different regions of the video content are treated differently based on their local texture characteristics. Regions with smooth textures or uniform patterns are identified as less prone to perceptible artifacts and are processed with lower computational intensity, while regions with complex textures receive more focused assessment.

Inventive Principle:

Principle #3Local quality

2Productivity

If texture information is used to focus assessment on perceptible regions, then computational efficiency is improved, but the complexity of the assessment system increases

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

Texture information is extracted and region classification is performed as a preliminary step before the main artifact assessment process. This preliminary action creates a map of perceptible and non-perceptible regions that guides subsequent processing, allowing the system to optimize resource allocation without requiring complex real-time decisions during artifact detection.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Texture information serves as an intermediary that bridges the gap between the video content and the artifact assessment process. By first analyzing texture characteristics and classifying regions accordingly, the system creates an intermediate representation that simplifies the overall assessment process and enables more efficient resource allocation.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250342689A1Artifact processing in video using texture information

Publication Date: 2025.11.06 BEIJING YOJAJA SOFTWARE TECHNOLOGY DEVELOPMENT CO LTD

AI summary

In some embodiments, a method receives an image to analyze for artifacts. Texture information that characterizes texture in the image is determined. The method merges the texture information with the image. The texture information is used to focus an analysis of artifacts in regions of the image. The method outputs a score based on the processing of the image that assesses the artifacts in the image.