DNN Cross-Component Prediction for Chroma Compression
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Solution Overview
Problem
Traditional video coding standards struggle with cross-component prediction, particularly in intra-prediction, as they fail to leverage the nonlinearity and nonlocal spatiotemporal correlations effectively, leading to suboptimal compression performance.
Innovation Solution
Employ a Deep Neural Network (DNN)-based Cross Component Prediction (CCP) model that utilizes luma component information, quantization parameters, and side information to predict the chroma component, enhancing compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional block-based hybrid prediction methods are used, then device complexity is reduced and ease of operation is improved, but compression performance deteriorates due to inability to exploit nonlinearity and nonlocal correlations
Solution Approach 1:
The patent replaces traditional mechanical block-based prediction algorithms with a Deep Neural Network system that processes pixel neighborhoods. The DNN extracts spatiotemporal stimuli and correlations nonlocally, substituting the rigid block-based mechanical approach with a flexible neural network-based approach that achieves superior compression performance while handling arbitrary block shapes.
2Productivity
If DNN-based cross component prediction is implemented, then compression performance is improved by exploiting nonlinearity and nonlocal correlations, but device complexity increases
Solution Approach 1:
The patent changes the operational parameters of the prediction system by introducing a DNN that learns optimal prediction parameters from data. Instead of fixed block-based parameters, the system adapts parameters dynamically through neural network processing, enabling exploitation of nonlinearity and nonlocal correlations to achieve higher compression ratios despite increased computational requirements.
3Measurement precision
If traditional cross component linear prediction is used, then device complexity is maintained at low levels, but prediction accuracy deteriorates due to inability to handle arbitrary shapes and nonlocal correlations
Solution Approach 1:
The patent transitions from traditional two-dimensional block-based prediction to a neural network approach that operates in higher-dimensional feature spaces. The DNN processes pixel neighborhoods and extracts spatiotemporal stimuli across multiple dimensions, enabling accurate prediction for arbitrary block shapes by lifting the problem from simple 2D block processing to multidimensional neural feature extraction.
Data Source
AI summary
Systems and methods for deep neural network (DNN)-based cross component prediction are provided. A method includes inputting a reconstructed luma block of an image or video into a DNN; and predicting, by the DNN, a reconstructed chroma block of the image or video based on the reconstructed luma block that is input. Luma and chroma reference information and side information may also be input into the DNN to predict the reconstructed chroma block. The various inputs may also be generated using processes such as downsampling and transformation.