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Related Experiment Video

Updated: Jul 7, 2026

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Spatial noise shaping based on human visual sensitivity and its application to image coding.

Shyh-shiaw Kuo¹, James D Johnston

¹AT&T Labs-Research, Florham Park, NJ 07932, USA. skuo@ieee.org

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

|February 5, 2008

Summary

This study introduces a spatial noise shaping (SNS) method that leverages human visual sensitivity to hide image noise in less perceptible areas, enhancing perceived image quality. The technique improves image coders, resulting in visually superior decoded images.

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Area of Science:

Digital image processing
Human-computer interaction
Visual perception

Background:

Image processing often introduces noise, degrading visual quality.
Existing methods may not optimally mask noise according to human visual perception.
Noise can be separable (e.g., watermarking) or nonseparable (e.g., quantization).

Purpose of the Study:

To present a novel spatial noise shaping (SNS) method.
To exploit human visual sensitivity for noise management.
To improve the perceived quality of processed images.

Main Methods:

Developed an SNS method utilizing frequency domain linear prediction.
Employed spatial envelope retrieval to identify areas of low visual sensitivity.

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Integrated the SNS method into image coders.

Main Results:

The SNS method effectively shapes noise into visually imperceptible regions.
Images decoded using SNS-enhanced coders exhibit superior perceived quality.
Demonstrated effectiveness in enhancing image coders.

Conclusions:

The proposed SNS method successfully reduces visually perceptible noise.
Leveraging human visual sensitivity is an effective strategy for image quality enhancement.
SNS offers a significant improvement in perceived image quality for coded images.