Dynamic Symbol Encoding for Adaptive Image Compression
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Solution Overview
Problem
Existing image compression techniques often use pre-specified coding types for symbols, which may not result in optimal compression due to fixed bit allocation, leading to inefficient use of resources and increased data size.
Innovation Solution
A method and apparatus for dynamically determining and encoding the optimal coding type for each symbol based on cost analysis, allowing for flexible bit allocation by including an indicator of the coding type in the compressed bitstream, enabling more adaptable compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pre-specified coding types are used for symbols, then device complexity is reduced, but compression efficiency deteriorates due to fixed bit allocation
Solution Approach 1:
The patent applies dynamics by transitioning from static pre-specified coding types to dynamic selection of coding types based on actual symbol characteristics. The encoder analyzes each symbol's probability distribution and selects the most appropriate coding type (e.g., fixed-bit, range coding, arithmetic coding) adaptively, allowing the system to optimize compression for each symbol while maintaining manageable complexity through systematic selection criteria.
Solution Approach 2:
The patent changes the parameter of coding type selection from fixed to variable. By introducing multiple coding types with different bit allocation strategies and dynamically selecting among them based on symbol properties, the system adjusts the coding parameters to match the actual data characteristics, thereby improving compression efficiency without excessive complexity increase.
2Device complexity
If fixed bit allocation is used for symbols, then device complexity is reduced, but compression efficiency deteriorates
Solution Approach 1:
The patent changes the bit allocation parameter from fixed to adaptive. Different coding types are employed with different bit allocation strategies: fixed-bit coding for simple cases, range coding for moderate complexity, and arithmetic coding for high compression needs. The system selects the appropriate bit allocation scheme based on symbol probability and context, optimizing compression efficiency while maintaining reasonable device complexity.
Solution Approach 2:
The patent introduces dynamic bit allocation by allowing the bit width and allocation strategy to vary according to the symbol being encoded. Rather than applying a uniform fixed-bit scheme, the system adapts the bit allocation dynamically based on the selected coding type and symbol characteristics, improving compression efficiency without significantly increasing overall system complexity.
3Productivity
If dynamic coding type selection is implemented, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the coding process into distinct stages: symbol probability analysis, coding type evaluation, selection based on cost function, and execution of selected coding type. This segmented approach breaks down the complex decision-making process into manageable steps, allowing dynamic coding type selection to achieve high compression efficiency while keeping device complexity controlled through systematic processing.
Solution Approach 2:
The patent manages complexity by systematically changing coding parameters (coding type, bit allocation, precision) based on analyzed symbol characteristics. The dynamic selection process evaluates multiple parameters and adjusts them according to a cost function, achieving high compression efficiency through reasoned parameter changes rather than exhaustive complexity.
Data Source
AI summary
Encoding an image includes determining respective costs of coding a symbol using available coding types. A first coding type indicates that a value of the symbol is to be decoded using a same number of bits, and a second coding type indicates that the value of the symbol is to be decoded using a range. An optimal coding type of the available coding types is selected, which corresponds to a smallest cost of the respective costs. A first indicator of the optimal coding type and a first symbol value of the symbol using the optimal coding type are encoded in a compressed bitstream. Decoding an image includes decoding, from a header of a compressed bitstream, respective coding types of symbols encoded in the compressed bitstream and decoding, from the compressed bitstream, respective values of the symbols according to the respective coding types decoded from the header.