Video Coding Block Merging Using Adaptive Merge Lists
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Solution Overview
Problem
Current video compression techniques, such as H.264/AVC, HEVC, and VVC, face inefficiencies due to increasing image sizes, resolutions, and frame rates, necessitating improved coding efficiency and image enhancement.
Innovation Solution
A video coding method and apparatus that adaptively generate a block merge list by referencing encoding information of the current block and its spatially and temporally adjacent blocks, using deep learning-based classification models to predict and transform the current block.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video compression techniques (H.264/AVC, HEVC, VVC) are used, then video data can be compressed and transmitted, but coding efficiency becomes insufficient as image size, resolution, and frame rate increase
Solution Approach 1:
The patent segments the video processing task by dividing the current block into multiple sub-blocks and performing separate prediction and transformation operations on each sub-block. This segmentation allows for more precise local processing, improving coding efficiency while managing the increasing data amount through distributed computation.
Solution Approach 2:
The patent introduces a new dimension of processing by applying deep learning-based classification models to determine merge list types and transformation parameters. This adds an intelligent decision-making layer that adapts processing strategies based on content characteristics, thereby improving coding efficiency without proportionally increasing computational burden.
2Productivity
If deep learning-based image processing techniques are applied to existing encoding techniques, then coding efficiency is improved, but computational complexity and resource requirements increase
Solution Approach 1:
The patent applies deep learning techniques selectively rather than universally - using classification models only for determining merge list types and transformation parameters where they provide the most benefit. This partial application improves coding efficiency while avoiding the excessive computational complexity of applying deep learning to every encoding operation.
Solution Approach 2:
The patent performs preliminary classification using deep learning models to determine the appropriate merge list type and transformation parameters before actual block processing. This preliminary action allows the system to select the most efficient processing path in advance, reducing overall computational complexity during the main encoding operation.
3Adaptability or versatility
If block merge lists are generated using predefined rules, then the process is simple and fast, but adaptability to different block types and content characteristics is limited
Solution Approach 1:
The patent makes the merge list generation process dynamic by using deep learning-based classification models that adapt to different block types and content characteristics. The system dynamically selects appropriate merge list types and transformation parameters based on learned patterns, achieving high adaptability while maintaining reasonable processing complexity through efficient model design.
Solution Approach 2:
The patent changes key parameters (merge list type, transformation type, transformation direction) based on classification results from deep learning models. These parameter changes allow the system to adapt to different block types and content characteristics, improving versatility without requiring completely different processing algorithms for each case.
Data Source
AI summary
A video coding method and an apparatus using block merging are presented. The video coding method and apparatus adaptively generate a block merge list, to predict and transform a current block, by referencing encoding information of the current block and by referencing encoding information of spatially and temporally adjacent blocks.