Descriptor Array Subdivision for Image Analysis Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image analysis methods require a large amount of data processing, particularly in mobile terminals, due to the high number of keypoints and SIFT descriptors, leading to inefficiencies and increased computational load, with current solutions like Google Goggles shifting the burden to the server and still requiring significant data transmission.
Innovation Solution
The method involves subdividing descriptor arrays into sub-arrays based on correlation relationships among color gradient histograms and compressing them using vector quantization, with decompression accounting for statistical spatial correlations to reduce data processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SIFT descriptors are used for accurate image comparison, then image recognition accuracy is improved, but data processing load and transmission requirements increase significantly
Solution Approach 1:
The descriptor array is divided into multiple sub-arrays, each representing different spatial regions or feature dimensions. This segmentation allows independent processing and compression of each sub-array, reducing the overall data volume while preserving essential recognition information through selective retention of prominent sub-arrays.
Solution Approach 2:
Only the most prominent and informative sub-arrays are extracted and retained for image recognition, while less important sub-arrays are discarded or heavily compressed. This extraction principle selectively keeps the essential features needed for accurate comparison, eliminating redundant data.
2Ease of operation
If all keypoints and descriptors are processed in mobile terminals, then local image analysis capability is improved, but computational load and energy consumption increase
Solution Approach 1:
Processing is divided into two stages: mobile terminal performs lightweight segmentation and extraction of prominent sub-arrays, while server performs comprehensive processing of the reduced data set. This segmentation of processing tasks reduces mobile computational load while maintaining local analysis capability.
Solution Approach 2:
The mobile terminal performs preliminary processing by extracting and transmitting only the most prominent sub-arrays before server-side processing. This preliminary action reduces the data volume that requires heavy computational processing, thereby reducing energy consumption while maintaining analysis capability.
3Quantity of substance
If descriptor arrays are compressed using traditional methods, then data transmission volume is reduced, but image analysis accuracy deteriorates
Solution Approach 1:
Compression is applied selectively to different sub-arrays based on their importance. Prominent sub-arrays are retained with minimal or no compression, while less important sub-arrays undergo aggressive compression. This segmented approach reduces overall data transmission volume while preserving the accuracy-critical information.
Solution Approach 2:
Different compression strategies are applied to different parts of the descriptor array based on local importance. High-quality retention is applied to prominent sub-arrays that are critical for accuracy, while lower-quality compression is applied to less important regions, optimizing the trade-off between transmission volume and analysis accuracy.
Data Source
AI summary
A method for processing an image, including: identifying a group of keypoints in the image; for each keypoint, calculating a corresponding descriptor array including plural array elements, each array element storing values taken by a corresponding color gradient histogram of a respective sub-region of the image in the neighborhood of the keypoint; for each keypoint, subdividing the descriptor array in at least two sub-arrays each including a respective number of elements of the descriptor array, and generating a compressed descriptor array including a corresponding compressed sub-array for each of the at least two sub-arrays, each compressed sub-array obtained by compressing the corresponding sub-array by vector quantization using a respective codebook; exploiting the compressed descriptor arrays of the keypoints for image analysis. For each keypoint of the group, the subdividing is based on correlation relationships among color gradient histograms with values stored in the elements of the descriptor array of each keypoint.