Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
Encoding01:19

Encoding

Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

You might also read

Related Articles

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

Sort by
Same author

Resolution enhancement of color video sequences.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

High performance scalable image compression with EBCOT.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Bit allocation for joint source/channel coding of scalable video.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

View generation for three-dimensional scenes from video sequences.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·1997
Same author

Deconvolution: a novel signal processing approach for determining activation time from fractionated electrograms and detecting infarcted tissue.

Circulation·1996
Same author

Halftone to continuous-tone conversion of error-diffusion coded images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·1995

Related Experiment Video

Updated: Jul 7, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Orientation adaptive subband coding of images.

D Taubman1, A Zakhor

  • 1Dept. of Electr. Eng. and Comput. Sci., California Univ., Berkeley, CA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1994
PubMed
Summary

This study introduces an orientation adaptive subband coding scheme for images. The new method significantly enhances image quality at low bit rates by reducing Gibbs-like artifacts, outperforming JPEG compression.

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Jul 7, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

  • Digital image processing
  • Signal processing
  • Information theory

Background:

  • Conventional subband coding schemes exhibit Gibbs-like phenomena at image edges, especially at low bit rates.
  • The directionality of local image features remains largely unexploited in existing subband coding techniques.

Purpose of the Study:

  • To develop a novel subband coding scheme that leverages the orientation of local image features.
  • To mitigate Gibbs-like artifacts in reconstructed images.
  • To improve subjective image quality compared to conventional methods.

Main Methods:

  • An orientation adaptive subband coding scheme was proposed.
  • The scheme utilizes information about the orientation of local image features.
  • Performance was evaluated against conventional separable subband coding and JPEG compression.

Main Results:

  • The orientation adaptive scheme effectively avoids objectionable Gibbs-like phenomena at reconstructed image edges.
  • At comparable bit rates, subjective image quality is considerably enhanced.
  • The proposed scheme shows superior performance over conventional separable subband coding and JPEG.

Conclusions:

  • Exploiting local image feature orientation in subband coding significantly improves image reconstruction quality.
  • The orientation adaptive scheme offers a viable solution for high-quality image compression at low bit rates.