Transform Coefficient Coding for Efficient Residual Decoding
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality images and videos, particularly in virtual and augmented reality, requires a more efficient compression technique to reduce transmission and storage costs.
Innovation Solution
The method and apparatus improve image coding efficiency by performing residual coding in units of transform blocks, determining the decoding order of parity level flags, and limiting the number of context-based coded flags to enhance coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high resolution and high quality image/video are used, then image quality is improved, but transmission and storage costs are increased
Solution Approach 1:
The patent extracts and transmits only the residual information (difference between original and predicted blocks) rather than the complete high-resolution image data. By performing transform coding on residual blocks and transmitting only significant transform coefficients, the system maintains high image quality while dramatically reducing the amount of data requiring transmission and storage.
Solution Approach 2:
The patent changes the representation parameters of image data by transforming residual blocks into frequency domain coefficients and applying quantization. This parameter transformation allows the same visual information to be represented with fewer bits, reducing transmission and storage costs while preserving image quality through selective precision allocation.
2Measurement precision
If residual coding is performed in units of sub-blocks, then coding precision is improved, but device complexity and processing overhead are increased
Solution Approach 1:
The patent segments the transform block into multiple sub-blocks and performs residual coding on each sub-block independently. This segmentation allows for more precise local coding where different regions can be coded with different levels of detail, improving overall coding precision while the modular structure helps manage processing complexity through systematic organization.
Solution Approach 2:
The patent applies partial action by performing detailed residual coding only on sub-blocks that contain significant content, while using simpler coding for other regions. This selective approach maintains high coding precision where needed while reducing processing overhead in less critical areas, balancing precision and complexity.
3Measurement precision
If the number of context-based coded flags is increased, then coding accuracy is improved, but the amount of data to be coded is increased
Solution Approach 1:
The patent changes the coding parameters by using context-based adaptive coding for transform coefficient flags. Instead of using fixed-length codes, the system adapts the coding parameters based on the statistical context and probability of different flag values, achieving higher coding accuracy while efficiently managing the amount of data through variable-length encoding.
Solution Approach 2:
The patent implements feedback mechanisms where the coding process continuously adapts based on previously decoded information. The context models are updated based on the sequence of decoded flags and coefficients, allowing the system to improve coding accuracy over time while managing data efficiency through learned statistical patterns rather than transmitting all possible information.
Data Source
AI summary
A method by which a decoding device decodes an image, according to the present invention, comprises the steps of: receiving a bitstream including residual information; deriving a quantized transform coefficient for a current block on the basis of the residual information included in the bitstream; deriving a residual sample for the current block on the basis of the quantized transform coefficient; and generating a restored picture on the basis of the residual sample for the current block.