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Related Concept Videos

Perceptual Constancy01:12

Perceptual Constancy

Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
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Related Experiment Video

Updated: Jul 7, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Locally adaptive perceptual image coding.

I Höntsch1, L J Karam

  • 1Telecommunications Research Center, Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287-7206, USA. hontsch@asu.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 12, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel perceptual image coder that adapts quantization to human visual masking properties. This locally adaptive approach improves image compression quality and bit rate without extra data, outperforming non-adaptive methods.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Existing image compression methods prioritize mathematical distortion metrics over perceptual quality.
  • Non-perceptual metrics fail to correlate with perceived quality at lower bit rates and may not preserve crucial visual details.

Purpose of the Study:

  • To develop a perceptual image coder that enhances compression performance by focusing on visually relevant components.
  • To improve image and video compression efficiency and perceived quality, especially at lower bit rates.

Main Methods:

  • A locally adaptive perceptual quantization scheme is proposed, exploiting human visual masking properties.
  • Visual masking thresholds are derived adaptively within subband decomposition.
  • Quantizer reconstruction levels are adjusted based on local masking in each subband transform coefficient.

Main Results:

  • The locally adaptive algorithm demonstrates superior performance compared to non-locally adaptive methods.
  • The method achieves better quality and bit rate without requiring additional side information.
  • Perceptually redundant information is effectively removed, leading to a low-entropy image representation.

Conclusions:

  • The proposed locally adaptive perceptual quantization scheme offers significant advantages for image compression.
  • The approach is well-suited for perceptually lossless image compression due to its efficiency and lack of side information.
  • Exploiting human visual masking properties leads to more effective and perceptually relevant image compression.