Range Coding Virtual Sliding Window JPEG2000 Tier-1
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video compression systems face a challenge in finding a tradeoff between effective data compression and computational complexity, particularly in entropy coding, where arithmetic coding offers better compression rates but is excessively complex and consumes a significant portion of computational power.
Innovation Solution
Implementing a lightweight, adaptive arithmetic coding solution using range coding and a virtual sliding window algorithm to reduce complexity and improve hardware implementation efficiency, specifically applied to the Tier-1 coding block in JPEG2000 image compression standard.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If arithmetic coding is used for entropy coding, then compression rate is improved, but device complexity increases significantly
Solution Approach 1:
The patent replaces the complex MQ-coder arithmetic coding implementation with a simpler range coder that uses basic integer arithmetic operations. This 'cheap' alternative achieves comparable compression performance without requiring the complex probability estimation mechanisms and large lookup tables of traditional arithmetic coders, thereby reducing device complexity while maintaining compression effectiveness.
Solution Approach 2:
The patent changes the computational parameters and data structures used in arithmetic coding. Instead of using floating-point arithmetic and complex probability models, it employs fixed-point integer arithmetic with simplified probability representations. This parameter change maintains the core arithmetic coding compression capability while dramatically reducing computational complexity and hardware resource requirements.
2Loss of information
If MQ-coder Tier-1 coding is implemented, then compression effectiveness is improved, but computational power consumption increases
Solution Approach 1:
The patent extracts and removes the most computationally intensive components from the MQ-coder implementation, specifically the complex probability estimation algorithms and renormalization operations. By taking out these energy-consuming elements and replacing them with simpler integer-based probability tracking, the system maintains compression effectiveness while significantly reducing power consumption.
Solution Approach 2:
The patent substitutes complex mechanical-like computational operations (floating-point arithmetic, multi-precision integer operations, and complex control logic) with simpler digital logic operations (integer arithmetic, bit manipulation, and simplified state machines). This substitution reduces the computational power required while preserving the essential compression function.
3Loss of information
If complex entropy coding algorithms are used, then compression ratio is improved, but ease of manufacture decreases
Solution Approach 1:
The patent segments the entropy coding process into distinct, manageable modules: a simplified range coder module, a virtual sliding window probability estimation module, and a bit stream output module. Each module uses simple integer arithmetic and can be independently implemented and tested, making the overall system easier to manufacture and integrate into hardware while maintaining good compression ratios.
Data Source
AI summary
Information to be transmitted may be compressed using range coding for adaptive arithmetic coding with a virtual sliding window. Cost and complexity of coding may be reduced in some cases.