Entropy Encoding Constraints for Binarized Image Data Units
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Solution Overview
Problem
Existing video data encoding and decoding systems face challenges in efficiently managing the number of binarized symbols per output data unit, leading to potential issues with data stream constraints and encoding efficiency.
Innovation Solution
The proposed solution involves an entropy encoder that selectively encodes image data to generate encoded binarized symbols, with the ability to provide padding data to meet constraints on the number of symbols per output data unit. This is achieved through an attribute detector and selector that choose appropriate constraints based on detected encoding attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed constraint on the number of binarized symbols per output data unit is applied, then data stream compliance is ensured, but encoding efficiency deteriorates due to excessive padding data
Solution Approach 1:
The constraint on the number of binarized symbols per output data unit is made dynamic rather than fixed. The entropy encoder selectively applies different constraints based on the specific characteristics of each output data unit, allowing the system to adapt the symbol limit to match the actual data requirements. This dynamic approach eliminates the need for excessive padding while ensuring data stream compliance is maintained for each unit individually.
Solution Approach 2:
The system changes the parameter of the constraint value based on the encoding attributes of different output data units. By detecting specific attributes of each data unit and selecting appropriate constraint levels, the system optimizes the balance between compliance and efficiency. This parameter adaptation allows tighter constraints where possible and more relaxed constraints where needed, reducing overall padding requirements.
2Productivity
If the number of binarized symbols per output data unit is increased, then more image data can be encoded per unit, but the risk of violating data stream constraints increases
Solution Approach 1:
The entropy encoder incorporates feedback mechanisms that monitor the number of binarized symbols being generated for each output data unit. Based on this feedback, the system dynamically adjusts the encoding process to ensure the symbol count remains within acceptable constraints. This feedback loop allows the system to maximize data encoding capacity while automatically preventing constraint violations.
Solution Approach 2:
The system performs preliminary assessment of each output data unit's characteristics before finalizing the encoding process. By evaluating encoding attributes in advance and selecting appropriate constraint levels beforehand, the system can plan the encoding strategy to maximize symbol utilization while guaranteeing compliance. This preliminary action prevents the need for costly corrections after encoding.
Data Source
AI summary
Image data encoding apparatus, comprises an entropy encoder configured to selectively encode data items representing image data so as to generate encoded binarized symbols of successive output data units; the entropy encoder being configured to generate an output data stream subject to a constraint defining an upper limit to the number of binarized symbols that may be expressed by any individual output data unit relative to the size in bytes of that output data unit, in which the entropy encoder is configured to provide padding data, for each output data unit which does not meet the constraint, so as to increase the size in bytes of that output data unit in order to meet the constraint; the apparatus comprising: an attribute detector configured to detect an encoding attribute applicable to a given output data unit; and a selector configured to select, in response to the detected encoding attribute, a constraint, for use with the given output data unit, from two or more candidate constraints.