Image Block Prediction Partitioning for Bidirectional Compression
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Solution Overview
Problem
Existing image compression technologies face challenges in efficiently compressing high-resolution and high-quality images, particularly in handling bidirectional predictions and adjusting bit precision for improved compression efficiency and accuracy.
Innovation Solution
The method involves partitioning a current block into multiple partitions, deriving prediction blocks through bidirectional prediction, and applying weighted sums and illumination compensation to enhance prediction performance, while adjusting bit precision to match internal and output depths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bidirectional prediction is applied to each partition of a current block, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The current block is divided into multiple partitions (e.g., two partitions), and bidirectional prediction is applied independently to each partition. This segmentation allows the complex bidirectional prediction process to be distributed across multiple smaller regions, improving overall prediction accuracy while managing computational complexity through localized processing.
Solution Approach 2:
Different prediction modes and parameters are applied to different partitions based on local characteristics. Each partition can have its own motion vectors, weighting factors, and prediction modes selected independently, allowing the system to adapt to local variations in the image content and improve prediction accuracy without uniformly increasing complexity across the entire block.
2Measurement precision
If weighted prediction with multiple weight candidates is used, then prediction performance is enhanced, but processing overhead increases
Solution Approach 1:
A set of weight candidates is pre-defined and prepared in advance for bidirectional prediction. These weight candidates are stored and readily available for selection during the decoding process, eliminating the need for real-time calculation of weight values and reducing processing overhead while maintaining enhanced prediction performance.
Solution Approach 2:
Instead of calculating optimal weights dynamically, the system changes the parameter approach by using a discrete set of pre-defined weight candidates. The decoder selects from these predetermined options based on the specific partition and prediction mode, which simplifies the processing while still providing multiple weight options to enhance prediction performance.
3Measurement precision
If illumination compensation is applied to prediction blocks, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
Illumination compensation is applied separately to each partition's prediction blocks rather than to the entire current block. This segmentation allows the compensation process to be performed on smaller, localized regions with their own illumination characteristics, improving overall prediction accuracy while reducing the computational burden compared to processing the entire block uniformly.
4Productivity
If bit precision is adjusted to match internal and output depths, then compression efficiency is improved, but precision management complexity increases
Solution Approach 1:
The system changes the bit precision parameter to match the internal bit depth and output bit depth requirements. By adjusting the precision of prediction blocks and intermediate calculations to align with the specified internal and output depths, the system improves compression efficiency while maintaining consistent precision management across different processing stages.
Data Source
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AI summary
A method and a device for decoding/encoding an image, according to the present disclosure, may comprise: dividing a current block into a plurality of partitions including a first partition and a second partition; deriving a first prediction block of the first partition; deriving a second prediction block of the second partition; and deriving a prediction block of the current block on the basis of the first prediction block and the second prediction block. Here, at least one of the first prediction block or the second prediction block may be derived by using bidirectional prediction.