Hierarchical Bytestream Decoding for Parallel Sparse Data Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for encoding and decoding image and video data struggle with optimizing parallel processing and memory utilization, particularly for sparse data sets, as they require sequential decoding and intensive memory usage, making it difficult to separate and reconstruct data efficiently.
Innovation Solution
A hierarchical decoding method is introduced, where encoded data is divided into subsets with attribute metadata elements that allow for separate decoding and spatial location of each subset without relying on other parts, reducing memory usage and enhancing decoding speed by allowing parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sequential decoding is used to maintain data accuracy, then decoding precision is improved, but decoding speed deteriorates
Solution Approach 1:
The patent divides the encoded data stream into multiple independent subsets, each representing a separate region of the original data. Each subset can be decoded independently and in parallel, eliminating the sequential dependency while maintaining reconstruction accuracy through the hierarchical structure and metadata that preserves spatial relationships.
2Loss of information
If the entire data stream is decoded together, then data completeness is improved, but memory utilization deteriorates
Solution Approach 1:
The patent segments the complete data stream into multiple smaller subsets that can be processed and stored separately. This reduces the peak memory requirements compared to holding the entire data stream in memory simultaneously, while the hierarchical structure ensures that all subsets together preserve the complete original information.
Solution Approach 2:
The patent introduces a hierarchical dimension to the data structure, organizing subsets at different levels (e.g., coarse-resolution subsets and fine-resolution subsets). This multi-level organization allows efficient memory management by enabling selective loading and processing of different hierarchical levels based on available memory resources.
3Productivity
If parallel processing is implemented to improve decoding speed, then productivity is improved, but device complexity deteriorates
Solution Approach 1:
The patent structures the encoded data into independently decodable subsets that can be processed in parallel by multiple decoding units. The hierarchical organization and embedded metadata provide each subset with self-contained information needed for independent processing, enabling straightforward parallel implementation without requiring complex coordination between processing units.
Data Source
AI summary
There may be provided a method of decoding a received set of encoded data representing information that has been compressed, wherein the encoded data set is divided into a hierarchy of subsets, the method comprising: decoding at least one first subset to derive a respective set of attribute metadata elements; separately decoding a plurality of second subsets comprising data elements, wherein each second subset describes a region of the information that has been compressed; and, reconstructing the information that has been compressed from the data elements, wherein the region of the information that has been compressed is identified from the attribute metadata elements.