Adaptive Delta Encoding With Spatial Predictors for Data Compression
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Solution Overview
Problem
Current data encoding methods lack versatility in handling diverse data structures and fail to provide an optimal compression ratio, especially when encoding different data channels or frames, and do not efficiently utilize spatial information for encoding and decoding.
Innovation Solution
The proposed method employs Delta encoding algorithms that use various predictors, including temporal and local spatial predictors, and entropy encoding to generate encoded data with reduced entropy, allowing for efficient encoding and decoding of data without increasing the value range of original values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional Delta coding methods are used, then encoding simplicity is maintained, but compression ratio and versatility for diverse data structures are insufficient
Solution Approach 1:
The encoder dynamically selects between multiple prediction methods (temporal prediction, spatial prediction, and no prediction) based on the characteristics of the data being encoded. This dynamic adaptation allows the system to optimize for different data structures and patterns without requiring a completely different encoding approach for each case, thereby improving versatility while managing complexity through a unified framework.
Solution Approach 2:
The system changes the prediction parameter (whether to apply temporal or spatial prediction) based on the data characteristics. By adjusting this parameter dynamically, the encoder can adapt to different data structures and achieve better compression ratios without fundamentally changing the core Delta coding mechanism, thus improving versatility with controlled complexity.
2Productivity
If advanced prediction methods are used, then compression ratio is improved, but encoding complexity and computational requirements increase
Solution Approach 1:
The system applies prediction methods selectively rather than universally. It uses temporal prediction only when beneficial, spatial prediction only when appropriate, and sometimes no prediction at all. This partial application of advanced methods achieves improved compression efficiency where needed while avoiding the computational overhead of applying complex prediction to all data, thus balancing compression efficiency with encoding complexity.
Solution Approach 2:
The encoding process is segmented into distinct stages: prediction stage, residual calculation stage, and entropy encoding stage. Each stage can be independently optimized and controlled. This segmentation allows the system to apply advanced prediction methods only in the prediction stage without propagating their complexity throughout the entire encoding process, thereby improving compression efficiency while managing overall computational complexity.
3Adaptability or versatility
If multiple prediction methods are implemented, then adaptability to different data patterns is improved, but device complexity increases
Solution Approach 1:
The system dynamically selects between temporal prediction, spatial prediction, and no prediction based on data characteristics, allowing it to adapt to different data patterns without implementing all prediction methods simultaneously in a complex manner. This dynamic selection approach improves adaptability while managing implementation complexity through a unified decision framework.
Solution Approach 2:
The encoder implements a universal prediction framework that can handle both temporal and spatial prediction through a common architecture. This multi-functional design allows the same basic prediction mechanism to serve multiple purposes (temporal prediction, spatial prediction, or no prediction) by simply changing the input data source, thereby improving adaptability to different data patterns without proportionally increasing implementation complexity.
Data Source
AI summary
There is provided an encoder for encoding input data to generate corresponding encoded data. The encoder is operable to process the input and to encode at least a portion thereof using at least one Delta encoding algorithm, and to generate one or more predictors for use in encoding one or more subsequent portions of the input data, wherein the encoder is also operable to encode data generated by the at least one Delta encoding algorithm and the one or more predictors by employing at least one entropy encoding algorithm to generate the encoded data. There is provided a decoder for decoding the encoded data to generate corresponding decoded data; optionally, the decoded data and the input data are mutually similar.