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Related Experiment Video

Updated: Jun 15, 2026

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

Software designs of image processing tasks with incremental refinement of computation.

Davide Anastasia¹, Yiannis Andreopoulos

¹Department of Electronic & Electrical Engineering, University College London, WC1E 7JE, London, UK. d.anastasia@ee.ucl.ac.uk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|March 19, 2010

Summary

This study introduces a novel software design for image processing tasks, enabling graceful degradation under reduced computational budgets. This approach ensures scalable performance and quality for demanding applications.

Related Experiment Videos

Last Updated: Jun 15, 2026

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

Area of Science:

Computer Vision
Image Processing Algorithms
Software Engineering

Background:

Current software for demanding image processing tasks lacks graceful degradation when computational resources are limited.
This limitation hinders the full utilization of programmable platforms due to worst-case performance considerations.
Efficient image processing is crucial for real-time applications and resource-constrained environments.

Purpose of the Study:

To propose a platform-independent software design for computationally-demanding image processing tasks that offers graceful degradation.
To enable incremental computation for improved output quality and arbitrary termination.
To demonstrate significant performance scalability and energy-distortion trade-offs.

Main Methods:

Developed bitplane-based computation combined with an incremental packing framework.
Implemented incremental computation for block transforms, 2-D convolution, and frame-by-frame block matching.
Compared the incremental approach with non-incremental software realizations.

Main Results:

The proposed incremental framework achieves monotonic improvement in output quality with progressive processing.
For equivalent precision, the incremental approach offers comparable or faster execution times.
The software can be arbitrarily terminated, providing results at the computed precision.

Conclusions:

The proposed incremental software design provides significant performance scalability with graceful degradation.
This approach overcomes limitations of current image processing software in resource-constrained environments.
Enables efficient utilization of modern programmable platforms for demanding image processing applications.