Neural Network Processor Differential Bit Quantization
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Solution Overview
Problem
Conventional image processing methods face limitations in handling complex visual data, especially in varying environmental conditions and real-time processing, and struggle to achieve high accuracy and speed in image analysis.
Innovation Solution
An image processing system that splits low-resolution images into patch images, clusters them based on differentiating features, and uses a neural network processor to differentiate and quantify reference and query patch images using different numbers of bits, reducing computational resources while generating high-quality output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If uniform quantization is applied to all patch images, then processing simplicity is maintained, but computational resources are wasted on patches that do not require high precision
Solution Approach 1:
The patent applies different quantization precision to different patch images based on their semantic importance. Reference patches receive high-precision quantization (first number of bits) while query patches receive low-precision quantization (second number of bits). This local differentiation optimizes computational resources by allocating higher precision only where needed, resolving the contradiction between uniform processing simplicity and resource efficiency.
2Measurement precision
If all patch images are processed with high precision, then image processing accuracy is improved, but processing time increases
Solution Approach 1:
The system differentiates processing precision based on patch image type. Reference patches are processed with high precision to maintain accuracy, while query patches use low precision to reduce processing time. This selective approach resolves the contradiction by applying high precision only where it matters most for overall image reconstruction quality.
Solution Approach 2:
The patent segments patch images into reference patches and query patches based on their functional roles in the super-resolution process. This segmentation enables differentiated processing strategies where reference patches receive intensive processing and query patches receive minimal processing, thereby reducing total processing time while maintaining accuracy.
3Productivity
If patch images are clustered and differentiated into reference and query patches, then computational efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs clustering and differentiation of patch images as a preliminary action before the main super-resolution processing. By pre-categorizing patches into reference and query types, the system simplifies the subsequent processing stages and enables efficient differential quantization, achieving high computational efficiency despite the added preliminary complexity.
Data Source
AI summary
An image processing device is trained to cluster a plurality of patch images into a plurality of clusters, select a first patch image from each of the plurality of clusters as a reference patch image and select a second patch image from each of the plurality of clusters as a query patch image, and perform quantization on the reference patch image and the query patch image using different numbers of bits.