Gradient-Driven YUV Color Space for DNN Inference
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Solution Overview
Problem
Existing compression algorithms for deep neural networks (DNNs) in IoT devices are inefficient due to limited wireless bandwidth and are designed for human vision systems, leading to low compression ratios and high inference errors.
Innovation Solution
The Gradient-Driven YUV (G-YUV) color space optimizes RGB-to-YUV conversion weights based on DNN perceptual sensitivity, concentrating information in the Y channel and minimizing sensitivity to U and V channels, allowing for more aggressive compression without compromising inference accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional RGB color space is used for DNN inference, then all three channels carry critical information, but it is difficult to compress the images and videos due to limited wireless bandwidth
Solution Approach 1:
The patent transforms the color space from RGB to YUV by changing the parameter representation of color information. This parameter transformation allows the data to be compressed more effectively while maintaining the critical information needed for DNN inference, resolving the contradiction between maintaining accuracy and reducing data volume.
2Quantity of substance
If conventional YUV color space is used to enable aggressive compression, then most information is concentrated in the Y channel, but conventional YUV does not take into consideration the color perception of DNNs, leading to suboptimal compression for DNN applications
Solution Approach 1:
The patent introduces a feedback mechanism where the DNN's gradient information is used to guide the optimization of the RGB-to-YUV conversion matrix. By using the DNN's own sensitivity information as feedback, the system can optimize the color space transformation to maximize compression while preserving the specific color features that the DNN finds important, thus resolving the contradiction between compression ratio and inference accuracy.
3Productivity
If compression is applied to reduce bandwidth consumption, then wireless bandwidth efficiency is improved, but inference errors increase when using compression algorithms designed for human vision systems
Solution Approach 1:
The patent applies local quality optimization by customizing the compression strategy according to the specific DNN's color sensitivity characteristics. Instead of using a universal compression algorithm designed for human vision, the system adapts the compression parameters locally to match the DNN's specific needs, thereby maintaining high inference accuracy while achieving efficient compression.
Data Source
AI summary
Example method includes: transmitting a plurality of probe images from an IoT device at an edge network to a server hosting a target DNN, wherein the plurality of images are injected with a limited amount of noise to probe sensitivities of the target DNN to the red, green, and blue colors; receiving a feedback comprising a plurality of DCT coefficients unique to target DNN from the server hosting the target DNN; computing a plurality of color conversion weights based on the feedback received from the server; converting a set of real-time images from RGB color space to YUV color space using the plurality of color conversion weights unique to the target DNN; compressing the set of real-time images using a quantization table unique to the target DNN by the IoT device; and transmitting the compressed set of real-time images to the server hosting the target DNN for DNN inferences.