Adaptive Spatial Resampling for Machine Vision Optimization
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Solution Overview
Problem
Existing image and video compression technologies focus on human vision quality, limiting the performance of spatial resampling for machine vision applications, which require improved rate-distortion performance and compact representation of visual data for analysis tasks.
Innovation Solution
A computer-implemented method and system for training spatial resampling modules using deep learning techniques, including down-sampling and up-sampling processes, to optimize image compression, with adaptive resampling factors based on instance segmentation, and a framework for end-to-end optimization that improves compression ratio and machine vision performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image and video compression technologies are used, then human vision quality is maintained, but machine vision performance deteriorates due to focus on texture quality rather than analysis-oriented information
Solution Approach 1:
The patent applies local quality by differentiating between human vision quality requirements and machine vision requirements. The training framework optimizes the resampling module specifically for machine vision tasks by using analysis models (object detection, segmentation, etc.) as guidance, rather than uniformly optimizing for human perception. This allows different parts of the image processing pipeline to be optimized for different quality metrics - human vision vs. machine analysis accuracy.
Solution Approach 2:
The patent changes the optimization parameters from human vision quality metrics to machine vision performance metrics. The loss function is designed based on analysis model outputs rather than traditional PSNR or SSIM metrics. This parameter change enables the resampling module to preserve features that are critical for machine vision tasks such as object detection and segmentation, rather than merely preserving visual fidelity for human observers.
2Productivity
If down-sampling is performed to reduce storage and transmission bandwidth, then compression ratio is improved, but information loss increases affecting analysis accuracy
Solution Approach 1:
The patent implements feedback by using analysis models to evaluate the quality of resampled images and feeding this information back into the training process. The loss function is constructed based on the outputs of analysis models (object detection, segmentation, etc.), creating a closed-loop optimization system. This feedback mechanism ensures that the down-sampling process preserves information critical for downstream analysis tasks while achieving high compression ratios.
Solution Approach 2:
The patent applies preliminary action by training the resampling module in advance with a specialized loss function that anticipates the needs of downstream analysis tasks. Rather than optimizing for general visual quality, the module is pre-trained to preserve features that will be useful for object detection, segmentation, and other machine vision tasks. This preliminary optimization ensures that when compression is applied, the essential analysis features are preserved.
3Manufacturing precision
If spatial resampling is optimized for human vision, then texture quality is enhanced, but rate-distortion performance for machine vision deteriorates
Solution Approach 1:
The patent inverts the traditional optimization approach by not optimizing for human vision quality but rather for machine vision performance. Instead of using traditional quality metrics like PSNR or SSIM that correlate with human perception, the loss function is designed based on the outputs of analysis models. This inversion allows the system to prioritize preservation of analysis-critical features over visual fidelity, achieving better rate-distortion performance for machine vision applications.
4Loss of energy
If resampling factor is increased to improve compression, then bitrate is reduced, but analysis accuracy deteriorates due to excessive information loss
Solution Approach 1:
The patent applies dynamics by making the resampling factor adaptive rather than fixed. The system can dynamically adjust the resampling factor based on the specific image content and the requirements of downstream analysis tasks. This dynamic adjustment allows the system to use higher resampling factors (greater compression) when appropriate while maintaining analysis accuracy when higher fidelity is needed, optimizing the trade-off between bitrate and analysis accuracy for each specific case.
Data Source
AI summary
A computer-implemented method for training spatial resampling modules includes: down-sampling, by a down-sampling module, an input image data to generate a down-sampled image data; up-sampling, by an up-sampling module, the down-sampled image data to generate a first up-sampled image data; analyzing, by a plurality of analysis models corresponding to a plurality of tasks, the first up-sampled image data; and training the down-sampling module based on a loss function associated with the plurality of analysis models according to the input image data and the first up-sampled image data.