Cross-Component Linear Model Prediction for Video Encoding
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Solution Overview
Problem
Existing video coding standards face challenges in balancing video quality and bandwidth requirements, particularly in encoding high-resolution videos that require more bandwidth.
Innovation Solution
The proposed method uses luma samples to predict chroma samples through intra-prediction, employing a cross-component linear model (CCLM) to achieve flexible and efficient video encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher resolution videos are transmitted, then video quality is improved, but bandwidth requirements increase
Solution Approach 1:
The patent extracts and utilizes the correlation between luma and chroma components by applying cross-component linear model prediction. Instead of encoding chroma data independently, the method extracts predictive information from luma samples to reconstruct chroma blocks, thereby reducing the amount of data that needs to be transmitted while maintaining video quality.
Solution Approach 2:
The patent introduces luma samples as an intermediary to predict chroma samples. By using luma data as a mediator, the encoding process can reconstruct chroma information without transmitting all chroma data explicitly, thus reducing bandwidth requirements while preserving color accuracy in the decoded video.
2Quantity of substance
If video compression is applied, then bandwidth requirements are reduced, but video quality deteriorates
Solution Approach 1:
The patent implements feedback by using reconstructed luma samples from the decoding process to generate chroma predictions. The decoded luma information feeds back into the chroma reconstruction process, allowing the decoder to reconstruct chroma blocks with higher accuracy using the relationship between luma and chroma components, thereby improving video quality after compression.
Solution Approach 2:
The patent performs preliminary action by establishing the cross-component linear model during the encoding process and transmitting the necessary model parameters. This preliminary setup enables the decoder to efficiently reconstruct chroma data using luma samples without requiring additional complex processing, thus maintaining quality while reducing bandwidth.
3Productivity
If cross-component linear model prediction is used, then encoding efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent applies parameter changes by utilizing the linear relationship between luma and chroma components through configurable parameters (alpha and beta coefficients). By changing the representation of chroma data from direct encoding to linear prediction based on luma parameters, the method improves encoding efficiency while keeping the computational complexity manageable through standardized parameter updates.
Data Source
AI summary
Techniques are described of a linear model prediction mode. A set of reconstructed neighboring luma samples are determined. Two pairs of luma value and chroma value are determined according to N reconstructed neighboring luma samples, N reconstructed neighboring chroma samples, M reconstructed neighboring luma samples, and M reconstructed neighboring chroma samples. The minimum value of the N reconstructed neighboring luma samples is not less than the maximum luma value of the remaining reconstructed neighboring luma samples of the set of reconstructed neighboring luma samples. The maximum value of the M reconstructed neighboring luma samples is not larger than the minimum luma value of the remaining reconstructed neighboring luma samples of the set of reconstructed neighboring luma samples. M>1 and N>1. Linear model parameters are determined based on the two pairs of luma value and chroma value, and a predictive block is determined based on the linear model parameters.