Super Resolution Encoding Pattern Optimization via Hierarchical Self-Organizing Map
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Solution Overview
Problem
Current Super Resolution Encoding (SRE) techniques face limitations in achieving high-resolution patterns at 2400 DPI, as the number of distinct encoded values exceeds the available representation in 600 DPI intensity arrays, leading to inefficiencies in data compression and rendering.
Innovation Solution
The implementation of a hierarchical self-organizing pattern map (HSOPM) and synthesis of traversal (SOT) techniques to optimize SRE patterns by deriving interrelationships between patterns, assigning weights based on continuity, and selecting encoded values adaptively to fit within the available number of encoded values, thereby enhancing edge accuracy and smoothing in high-resolution scanning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If SRE patterns are optimized to achieve 2400 DPI high-resolution patterns, then edge accuracy and rendering quality are improved, but the number of distinct encoded values (65536) exceeds the available representation in 600 DPI intensity arrays (256 patterns)
Solution Approach 1:
The patent segments the 65536 possible SRE patterns into multiple groups or clusters, where each group is represented by a limited set of prototype patterns (e.g., 256 patterns for 8-bit quantization). This segmentation allows the system to manage the large pattern space by dividing it into manageable segments that can be fitted into the available encoded values.
Solution Approach 2:
The patent changes the parameter of pattern representation by transforming the 16-bit pattern values into a compressed format that fits within 8-bit or 10-bit intensity arrays. This parameter change involves encoding the essential characteristics of high-resolution patterns into a reduced parameter space that can be stored and transmitted efficiently.
2Manufacturing precision
If the number of SRE patterns is increased to 65536 for 2400 DPI, then rendering quality is improved, but data compression efficiency deteriorates due to exceeding available encoded values
Solution Approach 1:
The patent creates compressed representations or copies of the essential pattern information that fit within the available encoded value space. Instead of storing all 65536 patterns, the system stores a condensed version that captures the necessary rendering quality while maintaining compression efficiency.
Solution Approach 2:
The patent transitions from a 16-bit pattern space to an 8-bit or 10-bit encoded space by introducing a new dimensional approach to pattern representation. This dimensionality change allows the system to preserve rendering quality while adapting to the constraints of available encoded values.
3Manufacturing precision
If 16 sub-pixels are used per pixel for 2400 DPI, then edge accuracy is improved, but the complexity of pattern encoding increases significantly
Solution Approach 1:
The patent performs preliminary organization and classification of the 65536 SRE patterns before encoding, using hierarchical clustering or self-organizing maps to pre-group patterns into manageable categories. This preliminary action reduces the complexity of the subsequent encoding process by preparing the pattern data in an optimized structure.
Solution Approach 2:
The patent introduces an intermediary processing layer that mediates between the 16 sub-pixel representations and the final encoded output. This intermediary layer performs pattern recognition, clustering, and compression operations that simplify the encoding complexity while preserving edge accuracy.
Data Source
AI summary
Methods, systems, and computer-program products for optimizing SRE (Super Resolution Encoding) patterns. A hierarchical self-organizing pattern map (HSOPM) of SRE patterns can be derived, which illustrates interrelationships between consecutive SRE patterns. Such a hierarchical self-organizing map provides a first level of hierarchy, a second level of hierarchy, etc. Different weights can be assigned to different synthesis of traversal (SoT) according to the second level of hierarchy. The likelihood of the SRE patterns can then be calculated based on a fitness of continuity and the different weights, so as to subsequently select and encode an allowed number of the SRE patterns while replacing other patterns with a lower likelihood value with an immediate root and thereby adaptively optimize any number of the SRE patterns with respect to any number of values.