Quantized Coefficient Coding with Adaptive Context Switching
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Solution Overview
Problem
Existing video coding technologies fail to fully utilize the statistical characteristics between quantized coefficients at respective locations, leading to poor coding performance and inefficient data compression.
Innovation Solution
Implement adaptive binary arithmetic coding using context models to determine significant identifications and exponential-Golomb coding for quantized coefficients, adjusting the coding mode to better utilize these statistical characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If monotonous coding modes are used for quantized coefficients, then the coding process is simple, but coding performance is poor and statistical characteristics are not utilized
Solution Approach 1:
The patent applies dynamics by transitioning from static monotonous coding modes to dynamic adaptive coding modes. The encoder selectively switches between different coding modes (exponential-Golomb coding and truncated exponential-Golomb coding) based on the statistical characteristics of quantized coefficients, specifically using the significant coefficient density as a decision criterion. This dynamic adaptation allows the system to optimize coding performance for different coefficient distributions while maintaining implementation feasibility.
Solution Approach 2:
The patent changes the coding parameter (coding mode) based on the statistical characteristics of the input data. By calculating the significant coefficient density and comparing it against a threshold, the system dynamically selects between different coding parameters (full exponential-Golomb vs. truncated version), thereby adapting the coding process to the actual data distribution and improving overall coding efficiency.
2Ease of manufacture
If existing coding methods are used, then implementation is straightforward, but data compression efficiency is insufficient
Solution Approach 1:
The patent segments the coding process into two distinct paths based on the significant coefficient density threshold. When the density is below the threshold, exponential-Golomb coding is applied; when above the threshold, truncated exponential-Golomb coding is used. This segmentation allows the system to handle different coefficient distributions with appropriately optimized coding methods, improving overall compression efficiency while maintaining clear implementation logic.
Solution Approach 2:
The patent modifies the coding parameter (coding mode selection) based on the calculated significant coefficient density. By changing the coding approach dynamically according to data characteristics, the system achieves better compression ratios without significantly complicating the implementation, as the decision logic is based on a simple density calculation and threshold comparison.
3Productivity
If adaptive coding modes are implemented to utilize statistical characteristics, then coding performance improves, but device complexity increases
Solution Approach 1:
The patent applies local quality by applying different coding strategies to different regions of the coefficient data based on the significant coefficient density. Instead of using a uniform coding approach for all coefficients, the system locally adapts the coding method (exponential-Golomb or truncated exponential-Golomb) according to the statistical characteristics of the current coefficient block, thereby improving coding performance where it matters most while keeping the overall structure manageable.
Solution Approach 2:
The patent implements feedback by calculating the significant coefficient density from the quantized coefficients and using this calculated value to determine the appropriate coding mode. This feedback loop allows the system to adaptively select the optimal coding strategy based on actual data characteristics, improving coding performance while the feedback mechanism itself remains relatively simple (density calculation and threshold comparison).
Data Source
AI summary
Provided are a coefficient coding/decoding method, an encoder and a decoder. The method includes: parsing a bitstream by adaptive binary arithmetic coding using a first context model based on quantization coefficient positions to be decoded to obtain a value of a non-zero identification; if the value is a first value, parsing the bitstream by adaptive binary aritlunetic coding using a second context model to obtain values of X preset identifications; if the value of the X-th preset identification is the first value, determining a target order of exponential Golomb coding, and parsing the bitstream using an exponential Golomb decoding algorithm of the target order to obtain remaining values of reconstructed quantization coefficient absolute values; and determining reconstructed quantization coefficient absolute values corresponding to the quantization coefficient positions based on the value of non-zero identification, values of X preset identifications, and remaining values of reconstructed quantization coefficient absolute values.