Prediction-Based Compression for Disordered Data Streams
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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 prediction models 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 processing is performed, but storage size reduction is not achieved and file size may increase
Solution Approach 1:
The patent applies preliminary actions by pre-processing the input data through transformations (such as bit-reversal, byte-swapping, or data reordering) before compression to enhance compressibility. This prepares the data in advance to reveal patterns that conventional compressors can exploit, ensuring that compression is effective even for data that initially appears highly disordered.
Solution Approach 2:
The patent changes parameters by applying multiple different transformation types (e.g., bit-level, byte-level, block-level transformations) and selecting the one that produces the most compressible output. This parameter exploration allows the system to adapt to various data characteristics and find optimal compression opportunities that single-parameter approaches would miss.
2Quantity of substance
If compression is performed on small files, then compression processing occurs, but actual storage space is not reduced due to file system blocking
Solution Approach 1:
The patent merges multiple small files or data streams into a single combined stream before compression. By aggregating data from multiple sources, the system overcomes file system blocking limitations and achieves meaningful compression ratios that would be impossible on individual small files alone, while the combined stream maintains decompressibility to individual originals.
Solution Approach 2:
The patent transitions from compressing data in one dimension (individual files) to another dimension (multiple files combined), exploring compression opportunities across file boundaries. This dimensional shift allows the compressor to find patterns and redundancies that span multiple files, effectively bypassing file system blocking constraints.
3Productivity
If adaptive predictors are used to maintain prediction models, then compression efficiency improves, but processing time increases
Solution Approach 1:
The patent applies partial action by using adaptive predictors selectively rather than uniformly across all data. The system may apply adaptive prediction only to portions of the data stream where it provides significant benefit, or use simplified prediction models for certain data types, thereby reducing overall processing time while maintaining compression efficiency where it matters most.
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.