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Updated: Feb 15, 2026
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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
Published on: December 13, 2016
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Region-Based Prediction for Image Compression in the Cloud.
Summary
This study introduces a new semi-local image prediction method for cloud storage, improving image compression by exploiting correlations between images. The novel approach enhances rate-distortion performance over existing inter-coding solutions.
Area of Science:
- Computer Vision
- Image Processing
- Cloud Computing
Background:
- Increasing cloud image storage necessitates efficient compression techniques.
- Exploiting inter-image correlations is key for effective image compression.
Purpose of the Study:
- To propose a novel image prediction scheme for cloud storage.
- To leverage semi-local approaches for exploiting inter-image correlation.
Main Methods:
- Segmenting reference images into planar regions using local features and super-pixels.
- Compensating for geometric and photometric disparities between matched image regions.
- Generating multiple references and organizing them into a pseudo-sequence for differential encoding.
Main Results:
- The proposed semi-local method significantly improves rate-distortion performance.
- Demonstrated superior compression efficiency compared to state-of-the-art inter-coding solutions.
- Effective utilization of classical video coding tools for image compression.
Conclusions:
- The novel image prediction scheme offers substantial gains in image compression efficiency.
- Semi-local correlation exploitation is a promising direction for cloud image storage optimization.
- The method provides a competitive alternative to existing high efficiency video coding solutions.
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