Transform Coefficient De-Binarization With Gradual Parameter Updates
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Solution Overview
Problem
Existing video encoding and decoding methods experience abrupt variations in parameters used for entropy encoding and decoding of transformation coefficients, leading to inefficient bit allocation and reduced image quality.
Innovation Solution
A method is introduced to gradually update parameters during the binarization of transformation coefficient levels using techniques like Golomb-Rice or concatenate codes, ensuring smooth parameter variation and optimized entropy encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If parameters used in entropy encoding and decoding of transformation coefficients are updated abruptly, then the encoding method can adapt quickly to changing data characteristics, but the bit amount increases and image quality deteriorates due to inefficient bit allocation
Solution Approach 1:
The patent applies dynamics by making the parameter update process adaptive rather than static. The parameter update determination unit dynamically decides whether to update parameters based on actual data characteristics, and the update unit gradually adjusts parameter values over multiple encoding steps rather than making abrupt changes. This dynamic approach allows the system to adapt to changing data characteristics while maintaining stable encoding performance and preventing image quality deterioration.
Solution Approach 2:
The patent directly addresses parameter changes by controlling how encoding parameters are updated. Instead of abrupt parameter changes, the system implements gradual parameter updates where the update unit modifies parameter values incrementally based on data characteristics. This controlled parameter change approach maintains adaptability while preventing the bit allocation inefficiency and image quality loss that occur with abrupt changes.
2Productivity
If parameters are updated frequently to improve encoding efficiency, then compression performance may improve, but the complexity of the encoding system increases
Solution Approach 1:
The patent implements feedback through the parameter update determination unit that monitors data characteristics and decides whether parameter updates are necessary. This feedback mechanism prevents unnecessary updates, reducing system complexity while maintaining encoding efficiency. The system only updates parameters when data characteristics actually change, avoiding the complexity of continuous monitoring and updating.
Solution Approach 2:
The dynamic parameter update approach allows the system to balance complexity and efficiency by adapting the update frequency to actual needs. Rather than implementing a fixed high-frequency update schedule that would increase complexity, the system dynamically adjusts update timing based on data characteristics, maintaining encoding efficiency without unnecessarily increasing system complexity.
3Device complexity
If traditional binarization methods are used without gradual parameter updates, then the encoding process is simpler, but the bit amount generated during encoding increases
Solution Approach 1:
The patent applies parameter changes by modifying the binarization parameters gradually rather than using fixed traditional methods. The update unit changes binarization parameters incrementally based on data characteristics, which reduces the bit amount generated during encoding while keeping the process complexity manageable through controlled, gradual adjustments rather than complex reconfiguration.
Data Source
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AI summary
An apparatus (200) of decoding a video, a method of encoding a video and a computer-readable storage medium are disclosed. Embodiments obtain a bin string corresponding to a transformation coefficient level syntax element indicating a size of transformation coefficient included in a transformation unit from a bit stream. Embodiments determine whether to update a previous parameter by comparing a size of a previous transformation coefficient that is restored before a current transformation coefficient. Embodiments obtain the size of the current transformation coefficient by de-binarizing the current transformation coefficient level syntax element by using the obtained current parameter.