Model parameter derivation for prediction modes based on least mean square optimization
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently compressing video data while maintaining video quality, particularly in scenarios where lossy compression is required, due to the inherent redundancy in video data and limited bandwidth/storage resources.
Innovation Solution
Implementing cross-component prediction (CCP) modes that utilize a multi-tap model to determine samples of a second color component based on associated samples of a first color component, incorporating downsampling of reference areas to reduce computational and storage resources, and employing model parameters for chroma sample reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cross-component prediction with multi-tap model is implemented to improve video compression efficiency, then compression ratio is improved, but computational complexity increases
Solution Approach 1:
The patent divides the chroma block into multiple prediction blocks and processes each block separately using different prediction modes (LMC, MM, PM). This segmentation allows the system to apply complex multi-tap models only where needed while using simpler methods elsewhere, resolving the contradiction between compression efficiency and computational complexity.
Solution Approach 2:
The patent dynamically adjusts the number of taps in the multi-tap model and selects different prediction modes based on block characteristics and available resources. By changing parameters adaptively rather than using a fixed complex model everywhere, the system achieves good compression while controlling computational load.
2Device complexity
If downsampled reference area is used to determine model parameters, then computational and storage resources are reduced, but prediction accuracy may be affected
Solution Approach 1:
The patent applies downsampling selectively to the reference area used for parameter derivation, rather than to the entire reference data. This partial application of downsampling reduces computational and storage resources while maintaining sufficient accuracy for prediction, resolving the contradiction between resource usage and prediction accuracy.
3Reliability
If multiple prediction modes are supported to improve video quality, then video quality is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic mode selection where the encoder chooses among multiple prediction modes (LMC, MM, PM) based on block characteristics, available bandwidth, and quality requirements. This dynamic approach allows the system to provide high video quality when needed while reducing complexity in other scenarios, resolving the contradiction between video quality and encoding complexity.
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.