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

Downsampling01:20

Downsampling

460
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
460
Reducing Line Loss01:18

Reducing Line Loss

259
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
259
Upsampling01:22

Upsampling

470
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...
470
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

216
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
216
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

561
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
561
Sampling Methods: Overview01:06

Sampling Methods: Overview

1.1K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Training and Testing Texture Similarity Metrics for Structurally Lossless Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2024
Same author

Pattern-Based Reconstruction of K-Level Images From Cutsets.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same author

Human Skin Gloss Perception Based on Texture Statistics.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021
Same author

EgoCom: A Multi-Person Multi-Modal Egocentric Communications Dataset.

IEEE transactions on pattern analysis and machine intelligence·2020
Same author

Similarity of Scenic Bilevel Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2017
Same author

Effective and efficient subjective testing of texture similarity metrics.

Journal of the Optical Society of America. A, Optics, image science, and vision·2015
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Nov 24, 2025

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

655

Hierarchical Lossy Bilevel Image Compression Based on Cutset Sampling.

Shengxin Zha, Thrasyvoulos N Pappas, David L Neuhoff

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 28, 2020
    PubMed
    Summary
    This summary is machine-generated.

    We introduce hierarchical lossy cutset coding (HLCC) for bilevel images, improving compression quality and efficiency. This method adapts to image detail, offering better performance than existing techniques.

    More Related Videos

    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
    07:15

    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

    Published on: July 11, 2025

    1.4K
    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    18.0K

    Related Experiment Videos

    Last Updated: Nov 24, 2025

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

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    655
    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
    07:15

    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

    Published on: July 11, 2025

    1.4K
    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    18.0K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Data Compression

    Background:

    • Bilevel images with smooth boundaries present unique compression challenges.
    • Existing fixed-grid lossy cutset coding (LCC) lacks adaptability to local image detail.
    • Optimal compression requires methods that balance rate and distortion effectively.

    Purpose of the Study:

    • To develop an advanced lossy compression technique for bilevel images, particularly complex ones.
    • To enhance rate-distortion performance and visual quality compared to existing methods.
    • To introduce a method offering constant quality control irrespective of image content.

    Main Methods:

    • Proposing hierarchical lossy cutset coding (HLCC) by adapting grid size to local image detail.
    • Implementing enhancements: multiple connection bits, boundary presmoothing, stricter connectivity, and advanced probability estimation.
    • Developing a progressive variation for refined reconstruction with minimal overhead.

    Main Results:

    • HLCC demonstrates superior rate-distortion performance and visual quality across diverse bilevel images.
    • The adaptive grid approach provides constant quality controlled by a single distortion threshold parameter.
    • Achieved better performance than existing lossy techniques at lower bitrates than lossless JBIG/JBIG2.

    Conclusions:

    • HLCC offers a significant advancement in lossy bilevel image compression.
    • The method provides consistent quality and improved efficiency for complex bilevel images.
    • Proposed enhancements further boost reconstruction accuracy and perceptual quality.