Multi-Core Transform Selection for High-Resolution Video Compression
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Solution Overview
Problem
Existing video encoding and decoding methods lack flexibility in selecting transform kernels, leading to inefficient energy compression performance for high-resolution or high-quality image content.
Innovation Solution
A method and apparatus for video decoding and encoding that utilize multi-core transform kernels, allowing selection of transform kernels based on block size, direction, and encoding/decoding conditions, including horizontal and vertical transform kernels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed transform kernel is used for transformation and inverse transformation, then the encoding and decoding processes are simplified and consistent, but the energy compression performance is insufficient for high-resolution or high-quality image content
Solution Approach 1:
The patent applies dynamics by enabling the transform kernel to be dynamically selected based on block characteristics (size, shape, prediction mode) rather than using a fixed kernel. The decoder determines whether to use DST7, DCT8, or other transform kernels according to the block type and encoding conditions, making the transformation process adaptive and flexible to improve compression efficiency for different content types
Solution Approach 2:
The patent changes the transform kernel parameter based on block characteristics. Different transform kernels (DST7, DCT8, DCT2, etc.) are selected according to block size, shape (square or non-square), and prediction mode (intra or inter). This parameter change allows optimal compression performance for different block types while maintaining efficient encoding and decoding processes
2Loss of energy
If multi-core transform kernels are selected based on block size and direction, then the energy compression performance is improved, but the complexity of determining and managing transform kernels increases
Solution Approach 1:
The patent applies local quality by selecting different transform kernels for different regions (blocks) based on their local characteristics. Each block is evaluated independently for its size, shape, and prediction mode, and the appropriate transform kernel is applied locally to that block. This localized approach improves compression performance without requiring complex global optimization
Solution Approach 2:
The patent manages transform kernel complexity through systematic parameter changes based on block characteristics. The transform kernel selection follows clear rules: DST7 for certain intra prediction modes with specific block sizes, DCT8 for inter prediction modes, and other kernels for specific block shapes. This structured parameter change approach improves compression while maintaining manageable complexity through consistent selection criteria
Data Source
AI summary
A method and apparatus for performing transformation and inverse transformation on a current block by using multi-core transform kernels in video encoding and decoding processes. A video decoding method may include obtaining, from a bitstream, multi-core transformation information indicating whether multi-core transformation kernels are to be used according to a size of a current block; obtaining horizontal transform kernel information and vertical transform kernel information from the bitstream when the multi-core transformation kernels are used according to the multi-core transformation information; determining a horizontal transform kernel for the current block according to the horizontal transform kernel information; determining a vertical transform kernel for the current block according to the vertical transform kernel information; and performing inverse transformation on the current block by using the horizontal transform kernel and the vertical transform kernel.