Image Decoding Candidate List Construction for Compression Efficiency
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Solution Overview
Problem
High-resolution and high-quality image data requires more efficient encoding and decoding techniques to reduce the increased costs of transmission and storage associated with higher data volumes.
Innovation Solution
The method involves deriving candidate lists for image blocks using neighboring block motion information, previously reconstructed or encoded information, and generating prediction blocks to improve compression efficiency, specifically by initializing and managing these lists to optimize motion information inclusion and averaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution and high-quality image data is used, then image quality is improved, but transmission and storage costs increase
Solution Approach 1:
The image is divided into multiple blocks that are processed independently using candidate lists. Each block uses motion information from neighboring blocks to generate prediction candidates, allowing localized optimization rather than processing the entire high-resolution image uniformly, thus reducing overall data volume while maintaining quality.
Solution Approach 2:
Motion information from neighboring blocks is pre-collected and stored in candidate lists before actual prediction. Previously reconstructed motion information is also prepared in advance. This preliminary preparation enables efficient prediction without requiring extensive real-time computation, reducing transmission and storage requirements for high-quality images.
2Quantity of substance
If conventional image compression techniques are used, then data volume is reduced, but compression efficiency is insufficient for high-resolution images
Solution Approach 1:
The candidate list is dynamically constructed by combining motion information from spatial neighboring blocks and temporal previously reconstructed blocks. This dynamic adaptation allows the compression system to respond to local image characteristics, improving compression efficiency for high-resolution images where static conventional techniques fail.
Solution Approach 2:
Motion information from neighboring blocks serves as an intermediary to generate prediction candidates. Instead of directly compressing high-resolution image data, the system uses motion information as a mediator to create prediction blocks, which then serve as the basis for efficient compression, achieving better results than conventional direct compression methods.
3Measurement precision
If motion information from multiple sources is combined, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
Different neighboring blocks contribute motion information tailored to their specific spatial relationships with the current block. Each neighbor's motion information is weighted and combined based on its local relevance, improving prediction accuracy without uniformly processing all possible sources, thus managing computational complexity through localized optimization.
Solution Approach 2:
The system changes parameters by selectively including or excluding motion information from different neighboring blocks based on their availability and relevance. This parameter adjustment allows the system to adapt computational complexity to the specific encoding situation while maintaining prediction accuracy through the use of available motion information.
Data Source
AI summary
An image is decoded by deriving a first candidate list for the current block by using motion information of neighboring blocks of the current block; deriving a second candidate list for the current block by using previously reconstructed motion information; deriving a third candidate list by using the first candidate list and the second candidate list; and deriving a prediction block for the current block by using the third candidate list.