Segmented Image Compression With Class-Specific ROI Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image and video compression methods fail to optimize encoding for specific regions of interest in smart surveillance and machine vision tasks, leading to suboptimal compression efficiency and performance in tasks like object classification and recognition.
Innovation Solution
An image data compression method that segments images into regions, applies class-specific learning-based encoders, and multiplexes their outputs, optimizing each encoder for the characteristics of the identified regions, including agnostic and semantic content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional compression algorithms (JPEG, JPEG2000) are used, then compression speed is acceptable, but compression ratio is low and block effect is obvious
Solution Approach 1:
The image is divided into multiple blocks, and each block is further segmented into multiple regions based on gradient direction. This multi-level segmentation allows the algorithm to process different regions with appropriate compression strategies, achieving high compression ratios while maintaining image quality by preserving important edge information.
Solution Approach 2:
Different compression strategies are applied to different regions within the image blocks. Regions with important edge information (identified by gradient analysis) are preserved with higher quality, while smooth regions are compressed more aggressively. This local quality approach ensures high compression ratios without obvious block effects.
2Measurement precision
If image quality is prioritized over compression speed, then image quality is good, but compression speed is slow
Solution Approach 1:
The algorithm performs preliminary gradient calculation and region classification before compression. By pre-identifying edge regions and smooth regions, the algorithm can quickly apply appropriate compression strategies during the main compression phase, maintaining high image quality while improving compression speed through efficient preprocessing.
3Loss of information
If compression ratio is increased, then less data is transmitted, but block effect becomes more obvious
Solution Approach 1:
The algorithm transforms image data from spatial domain to frequency domain using DCT, and applies quantization with different parameters for different regions. By changing the representation parameters and applying region-specific quantization matrices, the algorithm achieves high compression ratios while minimizing visible block effects through perceptual optimization.
Data Source
Figure 1
Figure 2A~2C
Figure 3
AI summary
Device and method for image data compression using segmentation and classification, the method comprising the steps of: identifying regions in a received image comprised of image pixels; segmenting the image pixels into segmented regions, each segmented region corresponding to an identified region, and into an image background comprised of image pixels, if existing, not belonging to any of the identified regions; determining a class for each segmented image region from a plurality of predetermined image classification classes; applying an image learning-based encoder to each segmented image region, according to the determined class of each segmented image region, wherein a specific image learning- based encoder has been preselected for each of the image classification classes from a library of image learning-based encoders; outputting the encoded segmented image regions.