Compression Engine Sequential Data Packing
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Solution Overview
Problem
Conventional data compression techniques fail to effectively reduce storage space for highly disordered or naturally originated data, such as images and videos, due to inefficient prediction models and file storage blocking issues, leading to wasted time and energy in compression and decompression processes without significant storage size reduction.
Innovation Solution
The approach involves identifying and grouping sequentially efficiently compressible data sequences, transforming data to increase orderliness, and packing similar data streams together to optimize compression efficiency, using adaptive predictors and entropy encoding techniques to maintain a consistent prediction model and reduce disruptive sequences, thereby overcoming file storage blocking limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression algorithms are applied to highly disordered data, then compression time is consumed, but storage size reduction is not achieved
Solution Approach 1:
The patent applies preliminary actions by pre-processing data to identify highly disordered sequences before compression, and by pre-organizing data into groups of similar characteristics. This allows the compression algorithm to skip or simplify processing of incompressible data, avoiding wasted compression time while still achieving storage reduction for compressible portions.
Solution Approach 2:
The patent segments data into distinct groups based on disorder characteristics, separating highly disordered data from moderately disordered or structured data. This segmentation allows different compression strategies to be applied to different segments, preventing the entire dataset from being subjected to time-consuming compression attempts that would yield minimal results.
2Quantity of substance
If compression is performed on small files due to file storage blocking, then processing time is spent, but actual storage space is not reduced
Solution Approach 1:
The patent merges multiple small files or data sequences into larger consolidated data structures, overcoming the file storage blocking issue. By combining small files into bigger units, the compression algorithm can work more efficiently and achieve actual storage reduction rather than just processing time consumption.
Solution Approach 2:
The patent transitions from file-level compression to a higher dimensional approach by organizing and compressing data across multiple files or sequences simultaneously. This dimensional change allows the system to overcome individual file size limitations and achieve meaningful storage reduction through bulk compression of aggregated data.
3Quantity of substance
If conventional entropy encoding is used on random data, then file size may increase, but compression process is still executed
Solution Approach 1:
The patent performs preliminary analysis of data disorder characteristics before applying entropy encoding. By pre-identifying highly random or disordered data, the system can avoid or modify the application of complex entropy encoding algorithms that would increase file size, thereby simplifying the overall compression process while preventing file size expansion.
Solution Approach 2:
The patent applies different compression qualities or methods to different portions of data based on local characteristics. Highly disordered data receives minimal or no compression processing, while moderately disordered or structured data receives full entropy encoding treatment. This local differentiation prevents file size increase in problematic areas while maintaining compression effectiveness elsewhere.
Data Source
AI summary
Prediction-based compression engines are spoon-fed with sequentially efficiently compressible (SEC) streams of input data that make it possible for the compression engines to more efficiently compress or otherwise compact the incoming data than would be possible with streams of input data accepted on a TV-raster scan basis. Various techniques are disclosed for intentionally forming SEC input data streams. Among these are the tight packing of alike files or fragments into concatenation suitcases and the decomposition of files into substantially predictably consistent (SPC) fragments or segments that are routed to different suitcases according to their type. In a graphics-directed embodiment, image frames are partitioned into segment areas that are internally SPC and multidirectional walks (i.e., U-turning walks) are defined in the segment areas where these defined walks are traced during compression and also during decompression. A variety of pre-compression data transformation methods are disclosed for causing apparently random data sequences to appear more compressibly alike to each other. The methods are usable in systems that permit substantially longer times for data compaction operations than for data decompaction operations.