Adaptive Chroma Prediction for Video Compression
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Solution Overview
Problem
Current video compression technologies face challenges in accurately predicting chroma during the encoding and decoding process, leading to blurred reconstructed images due to insufficient downsampling methods, which result in lost details and reduced image quality.
Innovation Solution
A chroma prediction method that involves obtaining luma and chroma information, along with specific processing information for downsampling, to reconstruct transient image blocks and adjust resolutions accordingly, allowing for improved chroma prediction by adapting to different image block characteristics and reducing spatial redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If downsampling is performed on the entire image for compression, then data volume is reduced and storage/transmission efficiency is improved, but image details are lost and chroma prediction accuracy deteriorates
Solution Approach 1:
The image is divided into multiple image blocks, and each block is processed independently with its own downsampling strategy. This allows different parts of the image to retain different levels of detail, preserving chroma information where needed while reducing overall data volume.
Solution Approach 2:
Different downsampling strategies are applied to different image blocks based on their local characteristics. Image blocks with important chroma information use less aggressive downsampling or no downsampling, while other blocks use more aggressive downsampling, achieving local optimization of both quality and compression.
2Productivity
If uniform downsampling is applied to all image blocks, then processing complexity is reduced and encoding efficiency is improved, but image quality deteriorates due to loss of local characteristics
Solution Approach 1:
The downsampling strategy is made dynamic and adaptive rather than static and uniform. The encoder selects different downsampling methods for different image blocks based on their characteristics, allowing the system to adapt to local image features while maintaining overall encoding efficiency.
Solution Approach 2:
The downsampling parameters (such as downsampling ratio, filter type) are changed according to the specific characteristics of each image block. This allows optimization of both compression efficiency and image quality by adjusting parameters locally rather than using fixed uniform parameters throughout the entire image.
3Quantity of substance
If aggressive downsampling is used to reduce bit rate, then transmission efficiency is improved, but chroma information is lost and reconstructed image quality deteriorates
Solution Approach 1:
Different levels of downsampling aggressiveness are applied to different image blocks based on their chroma information importance. Blocks containing critical chroma information undergo less aggressive downsampling, while blocks with less important chroma content can tolerate more aggressive downsampling, thus reducing overall bit rate while preserving essential chroma information.
Solution Approach 2:
The downsampling parameters are adjusted according to the chroma information content of each image block. By changing parameters such as downsampling ratio and filter selection based on local chroma characteristics, the system achieves better preservation of important chroma information while maintaining reduced bit rate for transmission.
Data Source
AI summary
A prediction method includes: performing downsampling on a luma component of a to-be-processed image block to obtain an initial signal of the luma component; obtaining a residual signal of the luma component based on the obtained initial signal of the luma component; obtaining a residual signal of a chroma component of the to-be-processed image block based on the chroma component of the to-be-processed image block without downsampling. The method further includes: respectively performing a transform on the residual signal of the luma component and the residual signal of the chroma component, to obtain transform coefficients, performing quantization on the obtained transform coefficients, to obtain quantization coefficients, and performing entropy encoding on the obtained quantization coefficients to obtain a bitstream.