Search research articles

Search research articles

Related Experiment Videos

An embedded saliency map estimator scheme: application to video encoding.

Nicolas Tsapatsoulis¹, Konstantinos Rapantzikos, Constantinos Pattichis

¹Department of Computer Science, University of Cyprus, CY 1678, Cyprus. nicolast@ucy.ac.cy

International Journal of Neural Systems

|August 19, 2007

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Virtual Reality Gamification of Visual Search, Response Inhibition, and Visual Short-Term Memory Tasks for Cognitive Assessment: Experimental Study.

JMIR formative research·2025

Same author

Emergency Department Length of Stay Classification Based on Ensemble Methods and Rule Extraction.

Studies in health technology and informatics·2024

Same author

Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation.

Frontiers in big data·2023

Same author

eHealth4U: A DEMO of a Prototype National Electronic Health Record for Cyprus.

Studies in health technology and informatics·2023

Same author

Toward objective monitoring of Parkinson's disease motor symptoms using a wearable device: wearability and performance evaluation of PDMonitor<sup>®</sup>.

Frontiers in neurology·2023

Same author

Opinion Mining From Social Media Short Texts: Does Collective Intelligence Beat Deep Learning?

Frontiers in robotics and AI·2021

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

This study introduces a novel computational model for visual attention, integrating top-down and bottom-up processing for enhanced saliency mapping. The model improves video compression efficiency without compromising visual quality.

Area of Science:

Computer Vision
Signal Processing
Human-Computer Interaction

Background:

Visual attention models are crucial for understanding human perception and guiding computational systems.
Existing models often process top-down and bottom-up information separately.
Efficient computation of visual features remains a challenge in real-time applications.

Purpose of the Study:

To propose a novel saliency-based computational model for visual attention.
To integrate both top-down (goal-directed) and bottom-up (stimulus-driven) information processing.
To enhance video compression efficiency using the developed attention model.

Main Methods:

A unified multi-resolution framework using wavelet subband analysis for computing orientation, intensity, and color conspicuity maps.

Related Experiment Videos

Integration of a top-down skin conspicuity map (emulating face detection) with bottom-up feature maps.

Combination of maps via a sigmoid function to generate the final saliency map.

Implementation on a DSP platform for real-time embedded system operation.

Main Results:

The model successfully integrates top-down and bottom-up visual information processing.
Wavelet-based approach enables efficient computation of topographic feature maps.
Real-time operation achieved on a DSP platform.
Significant improvements in video compression efficiency (MPEG-1 and MPEG-4) were demonstrated without perceived loss in visual quality.

Conclusions:

The proposed saliency-based model offers an effective approach to visual attention.
The integrated framework and wavelet-based computation provide computational advantages.
The model shows practical utility in improving video compression, particularly in low bit-rate scenarios.