Cross-Component Chroma Prediction Using LMS Multi-Tap Modeling
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently compressing video data while maintaining quality, particularly in handling color components like chroma samples, leading to increased bandwidth and storage requirements.
Innovation Solution
Implementing a cross-component prediction (CCP) mode that uses a multi-tap model to determine chroma samples based on luma samples, including downsampling reference areas to reduce computational and storage resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-component prediction mode is implemented to improve chroma sample prediction accuracy, then video quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the reference area into multiple tap regions (first tap, second tap, third tap) with different weights. This segmentation allows the system to process and weight different spatial regions separately, improving prediction accuracy by capturing local variations while maintaining manageable computational complexity through structured processing.
Solution Approach 2:
The patent applies least mean square optimization to dynamically determine model parameters (weights for each tap) based on the specific video content. This parameter adaptation allows the prediction model to optimize its performance for different scenes and conditions, improving accuracy without requiring a fixed complex structure.
2Measurement precision
If multi-tap model with multiple reference samples is used to improve prediction accuracy, then chroma sample quality is improved, but memory storage requirements increase
Solution Approach 1:
The patent extracts only the necessary reference samples from the reference area for multi-tap prediction, rather than storing or processing all available samples. By selectively extracting and weighting specific tap regions, the system reduces memory requirements while maintaining prediction accuracy through focused use of the most relevant samples.
3Productivity
If downsampled reference area is used to reduce computational resources, then processing speed is improved, but prediction accuracy may deteriorate
Solution Approach 1:
The patent applies different weights to different local regions (taps) of the reference area, with the first tap having a first weight, second tap having a second weight, and third tap having a third weight. This local quality differentiation allows the system to prioritize certain regions over others, maintaining prediction accuracy for critical areas while reducing processing burden on less important regions.
Data Source
AI summary
The various implementations described herein include methods and systems for coding video. In one aspect, a video bitstream includes a current coding block of an image frame and signals a syntax element for a cross-component prediction (CCP) mode. When the CCP mode is enabled, a computing system identifies a reference area of the current coding block and downsamples the reference area to identify samples of a subset of reference area. A plurality of model parameters used in the CCP mode are determined for a first chroma sample of the current coding block based on the samples of the subset of reference area. The computing system combines a set of one or more luma samples (e.g., of a reference coding block) using the plurality of model parameters to generate the first chroma sample. The image frame is reconstructed based on the current coding block including the first chroma sample.