Subjective Artifact Assessment for Video Banding Mitigation
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Solution Overview
Problem
Existing video delivery systems face challenges in identifying and mitigating artifacts such as banding, blocking, blurring, and compression artifacts due to limited bit depth, without effective methods for precise measurement and mitigation.
Innovation Solution
A system that generates training data through subjective assessment tools, using client devices to collect user feedback on artifact visibility in images, combining image-level and region-level ratings to improve the training of processes for artifact identification, measurement, and mitigation, employing neural networks to adjust parameters based on user responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If limited bit depth (8 bits) is used to represent luminance and chrominance information, then data transmission and storage efficiency is improved, but visible banding artifacts increase
Solution Approach 1:
The patent introduces an intermediary assessment system that uses human subject feedback as a mediator between the compressed video signal and the optimization process. Multiple assessors view the video and provide feedback on perceived banding artifacts, which then guides the optimization of compression parameters to reduce artifacts while maintaining efficient bit depth
Solution Approach 2:
The patent implements a feedback mechanism where subjective quality assessments from human viewers are collected and used to adjust compression parameters. The system continuously refines compression settings based on feedback about perceived banding, creating a closed-loop optimization process that adapts to actual perceptual quality
2Measurement precision
If comprehensive artifact assessment methods are implemented, then artifact detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex artifact assessment task into multiple independent components: individual assessors evaluate specific aspects of video quality, each providing focused feedback on particular artifact types. This segmentation allows the system to achieve comprehensive assessment accuracy while keeping each individual assessment component relatively simple and manageable
Data Source
AI summary
In some embodiments, a method sends information for a sample of content, a first question, and a second question for output on an interface. The first question receives, from a subject, a first response for a sample level rating for an artifact that is perceived to be visible in the sample and the second question receives, from the subject, a second response for regions in the sample that are perceived to contain the artifact. The method receives the first response for the sample level rating and the second response for regions that are perceived to contain the artifact. First responses are combined from multiple subjects to generate an opinion score for the sample and second responses are combined to generate region scores for regions. The method generates training data from the opinion score and the region scores to train a process to perform an action based on the artifacts.