Smart Reordering in Recursive Block Partitioning for Video Coding
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Solution Overview
Problem
Current video encoding and decoding techniques face inefficiencies in data processing and transmission due to the large amount of data in digital video streams, particularly in identifying optimal prediction modes for blocks within frames, which limits coding efficiency and quality.
Innovation Solution
The method involves identifying and processing blocks within a frame into three groups for inter and intra prediction, reordering the encoding and decoding process to utilize reconstructed pixel information from previously encoded blocks, allowing for improved prediction and coding efficiency by using inter and intra prediction modes effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If blocks are encoded in traditional scan order (raster, diagonal, or zigzag), then the encoding process is simple and straightforward, but later blocks cannot utilize reconstructed pixel information from previously encoded blocks, limiting prediction quality
Solution Approach 1:
The patent segments blocks into three distinct groups based on their prediction mode requirements: Group 1 blocks encoded using inter prediction only, Group 2 blocks with mixed inter/intra sub-blocks, and Group 3 blocks encoded using intra prediction only. This segmentation allows the encoder to process blocks in an optimized order that maximizes prediction quality while maintaining manageable complexity through systematic organization.
Solution Approach 2:
The patent applies preliminary action by encoding and reconstructing Group 1 blocks first before processing Group 2 and Group 3 blocks. This ensures that reconstructed pixel information from inter-predicted blocks is available beforehand, enabling higher quality intra prediction for subsequent blocks without requiring complex real-time dependencies.
2Productivity
If all blocks are encoded using the same prediction mode, then the encoding process is uniform and simple, but coding efficiency is limited because optimal prediction modes vary by block characteristics
Solution Approach 1:
The patent applies local quality by assigning different prediction modes to different blocks based on their specific characteristics and spatial location. Group 1 blocks use inter prediction, Group 2 blocks use a combination of inter and intra prediction for their sub-blocks, and Group 3 blocks use intra prediction. This localized optimization of prediction modes significantly improves coding efficiency by matching the prediction method to the actual block content and dependencies.
Solution Approach 2:
The patent introduces dynamics by making the prediction mode selection adaptive and variable across different blocks rather than static and uniform. The encoder dynamically determines the appropriate prediction mode for each block based on rate-distortion optimization and spatial relationships, allowing the system to adapt to varying block characteristics and maximize overall coding efficiency.
3Manufacturing precision
If intra prediction uses only previously decoded blocks in traditional scan order, then data dependency is minimized for parallel processing, but prediction quality deteriorates because useful reconstructed information from later blocks is unavailable
Solution Approach 1:
The patent applies inversion by reversing the traditional approach: instead of encoding blocks in a fixed scan order and limiting intra prediction to only previously decoded blocks, it reorders encoding to process inter-predicted blocks first, then uses their reconstructed information for intra prediction of subsequent blocks. This inverted dependency structure maximizes the availability of useful reconstructed pixel information for prediction while the parallel processing architecture manages the processing time implications.
Data Source
AI summary
Video data streams can be encoded and decoded using inter or intra prediction. The blocks of a frame can be processed based on depth, from the lowest level sub-blocks to the highest level large blocks, and divided into groups of blocks that are inter predicted, blocks having sub-blocks that are inter predicted and sub-blocks that are intra predicted, and blocks that are intra predicted. The inter predicted blocks are encoded or decoded first, followed by the inter predicted sub-blocks, the intra predicted sub-blocks, and the intra predicted blocks. Data from the inter predicted blocks and sub-blocks may be used to improve the prediction performance of intra predicted blocks and sub-blocks.