Prediction-Based Compression with SEC Data Stream Reordering
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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 wasteful 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.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression algorithms are applied to highly disordered data, then compression process is performed, but storage size reduction is not achieved and file size may increase
Solution Approach 1:
The patent applies pre-compression transformations to reorder and transform the input data before it enters the compression engine. This preliminary action rearranges highly disordered data into a more predictable sequence, enabling the compression algorithm to achieve actual storage size reduction rather than producing larger files.
Solution Approach 2:
The patent transforms the input data parameters by applying mathematical transformations and reordering operations that change the statistical properties of the data. This makes the data more suitable for entropy encoding by reducing its entropy and creating patterns that compression algorithms can exploit.
2Quantity of substance
If compression is performed on small files, then compression process is executed, but storage blocking prevents actual storage reduction
Solution Approach 1:
The patent merges multiple small files into a single concatenated input stream before compression. This combining approach allows the compression algorithm to find patterns across file boundaries and achieve actual storage reduction, while the selective compression logic determines which merged files benefit from compression versus those that would waste time.
Solution Approach 2:
The patent applies different compression strategies to different portions of the input data based on local characteristics. Files or data segments with high predictability receive compression processing, while highly disordered segments are identified and handled differently to avoid wasting compression time without achieving storage reduction.
3Productivity
If adaptive predictors are used to maintain consistent prediction models, then compression efficiency is improved, but processing time increases
Solution Approach 1:
The patent performs pre-compression transformations that create more predictable data patterns before the adaptive predictor processes the input. This preliminary reordering reduces the computational burden on the adaptive predictor during compression, maintaining high compression efficiency while reducing the additional processing time that would otherwise be required.
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.