Photo Cluster Detection and Compression for Burst Mode Storage
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Solution Overview
Problem
High-resolution images captured in burst mode consume excessive storage space on information handling devices, as existing technologies lack efficient methods for compressing and managing large volumes of similar images.
Innovation Solution
An apparatus and method for photo cluster detection and compression, which uses a processor to identify and compress subsets of images based on timestamps, features, motion sensor data, and location, employing lossless compression algorithms to reduce storage space by removing redundant images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high resolution images are captured in burst mode, then image quality is improved, but storage space consumption increases
Solution Approach 1:
The patent segments the burst images into clusters based on similarity features (motion sensor data correlation, location data proximity, timestamp proximity). Within each cluster, only one representative image is retained while others are discarded, effectively reducing storage space while maintaining image quality through selective retention of representative images.
Solution Approach 2:
The patent discards redundant images within clusters by retaining only one representative image per cluster. The system recovers storage space by deleting duplicate or highly similar images while preserving the essential visual information through the retained representative images.
2Quantity of substance
If redundant images are removed from the dataset, then storage space is reduced, but information completeness may be compromised
Solution Approach 1:
The patent employs feedback mechanisms through correlation analysis of motion sensor data, location data, and timestamps to determine image similarity. This feedback loop ensures that only images with sufficient similarity (above threshold correlations) are deemed redundant and removed, while images providing unique information are retained, thus maintaining information completeness while reducing storage space.
Data Source
AI summary
Apparatuses, methods, systems, and program products are disclosed for photo cluster detection and compression. An image module receives a set of images captured using a camera. A subset module determines a subset of the set of images based on a timestamp associated with each image of the set of images. The subset of images includes a plurality of images having a timestamp within a predefined time range. A compression module compresses the subset of images such that the compressed subset of images uses less storage space than the subset of images.