Quantized Image Generation Using Trained Dither Matrix
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Solution Overview
Problem
Neural networks require significant resources to process data, leading to limitations in generating accurate outputs for untrained input patterns, particularly in image recognition tasks where information loss during quantization affects recognition performance.
Innovation Solution
A system that includes an image sensor, an image processor, and an output interface, utilizing a trained quantization filter, specifically a dither matrix with threshold values adjusted based on a training process to generate a quantized image with reduced bit depth, minimizing information loss and improving object recognition efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If image quantization is applied to reduce bit depth, then data transmission bandwidth and processing resources are reduced, but information loss occurs which degrades object recognition accuracy
Solution Approach 1:
The patent applies parameter changes by training the quantization filter with specific threshold values that are optimized for object recognition tasks. The filter parameters are adjusted during training to minimize information loss while maintaining recognition accuracy, allowing the system to achieve both low bit depth and high recognition performance.
Solution Approach 2:
The training process incorporates feedback mechanisms where the quantized images are fed back into the object recognition model to evaluate performance. Based on this feedback, the quantization filter parameters are iteratively adjusted to reduce information loss and maintain accurate object recognition, resolving the contradiction between compression and accuracy.
2Measurement precision
If neural network processing is used for object recognition, then recognition accuracy is improved, but processing resources and computational complexity increase significantly
Solution Approach 1:
The system performs preliminary action by pre-training the quantization filter with optimized threshold values before actual object recognition. This pre-processing prepares the image data in advance, converting it to a quantized format that reduces subsequent processing requirements while maintaining recognition accuracy, thus resolving the contradiction between accuracy and resource consumption.
Solution Approach 2:
The patent creates a simplified copy of the original image through quantization, reducing the bit depth while preserving essential features for recognition. This copied quantized image can then be processed with reduced computational resources, achieving the balance between recognition accuracy and processing efficiency.
Data Source
AI summary
A system includes: an image sensor configured to acquire an image; an image processor configured to generate a quantized image based on the acquired image using a trained quantization filter; and an output interface configured to output the quantized image.