Parallel Delta Row Image Decoding on Multi-Core Processors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
High-resolution image-forming apparatuses face memory and processing inefficiencies due to the need for large memory areas to store and process page-by-page image data, particularly when using delta row encoding, which can be cumbersome with traditional single-core processing systems.
Innovation Solution
The method employs multiple processors for parallel processing of delta-encoded image data, dividing pixels into blocks for delta accumulation and decoding, and subsequent image rendering, leveraging multi-core CPUs to enhance efficiency and reduce memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If delta row encoding is used for high-resolution images, then memory area is reduced, but processing complexity increases
Solution Approach 1:
The patent divides the image data into multiple bands in the column direction, with each band further divided into blocks that can be processed independently by different processors. This segmentation enables parallel processing of delta row encoded data, reducing the overall processing complexity while maintaining memory efficiency
Solution Approach 2:
The patent performs preliminary delta accumulation on blocks before full decoding is required. By pre-processing the delta data and storing accumulated results, the system reduces the computational burden during final image rendering, effectively managing processing complexity
2Productivity
If traditional single-core processing is used for delta row encoded images, then processing is simpler to implement, but processing speed decreases
Solution Approach 1:
The patent segments image data into multiple bands and blocks that can be assigned to different processor cores. This enables parallel processing across multiple cores, dramatically improving processing speed for high-resolution images while maintaining a relatively simple implementation approach
Solution Approach 2:
The patent introduces parallel processing across multiple processor cores, transitioning from single-core sequential processing to multi-core parallel processing. This dimensional change in processing architecture enables significant speed improvements without proportionally increasing implementation complexity
3Productivity
If image data is divided into multiple blocks for parallel processing, then processing speed improves, but data dependency between blocks increases
Solution Approach 1:
The patent performs preliminary delta accumulation on each block independently before combining results. This pre-processing step reduces data dependencies between blocks, as each block can be processed independently with its own accumulated delta values, simplifying parallel processing coordination
Solution Approach 2:
The patent uses accumulated delta data as an intermediary between block processing stages. By storing and retrieving these intermediate results, the system manages data dependencies efficiently, enabling parallel processing while maintaining data integrity across block boundaries
Data Source
AI summary
A method using L processers includes: receiving image data encoded by delta row encoding; dividing the two dimensionally arranged plurality of pixels by M (M≤L) to make M blocks of pixels; assigning M processers to perform a parallel processing of accumulating the delta data for all the row lines of each of the M blocks of the image data to obtain the accumulated delta data, the accumulated delta data including a total delta between the first row line and the last row line in each of the blocks; obtaining the first row lines in the respective blocks using the total delta one by one starting from the second block; and assigning M+1 processers to perform a parallel processing of decoding using the obtained first row lines to obtain the decoded data and a parallel processing of rendering the image data using the decoded data.