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

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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...
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

The CXCL9/SPP1 polarity axis in tumor-associated macrophages: immunoregulatory and prognostic significance in non-small cell lung cancer.

Frontiers in immunology·2026
Same author

Subspecialty-specific foundation model for intelligent gastrointestinal pathology.

NPJ digital medicine·2026
Same author

Multi-omics integration reveals that pyrimidine metabolism in lung adenocarcinoma drives an immunosuppressive microenvironment.

iScience·2026
Same author

Molecular classification and prognosis study of pancreatic ductal adenocarcinoma through multi-omics integrated clustering analysis.

PeerJ·2026
Same author

Next Bit Prediction: A Unified Lossless and Lossy Point Cloud Geometry Compression Framework.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Glycosylated IgG antibodies contribute to the recovery of haemorrhagic fever with renal syndrome patients.

eLife·2025
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: May 10, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Multiview-video-plus-depth coding based on the advanced video coding standard.

Miska M Hannuksela1, Dmytro Rusanovskyy, Wenyi Su

  • 1Nokia Research Center, Tampere 33720, Finland. miska.hannuksela@nokia.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 26, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new multiview video plus depth coding scheme, compatible with H.264/AVC and MVC standards. It achieves significant bitrate reduction for 3D video coding, enhancing efficiency.

Related Experiment Videos

Last Updated: May 10, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Multimedia Systems

Background:

  • Multiview video plus depth (MVD) data requires efficient compression for 3D video applications.
  • Existing standards like H.264/AVC and Multiview Video Coding (MVC) provide a foundation but need extensions for MVD.
  • The Moving Picture Experts Group (MPEG) standardization committee actively seeks efficient 3D video coding solutions.

Purpose of the Study:

  • To propose a novel multiview video plus depth coding scheme.
  • To ensure compatibility with existing advanced video coding standards (H.264/AVC and MVC).
  • To improve the bitrate efficiency for 3D video coding (3DV) applications.

Main Methods:

  • Development of encoding and in-loop coding tools specifically for depth and texture video.
  • Implementation of depth-based texture motion vector prediction.
  • Utilizing depth-range-based weighted prediction and joint inter-view depth filtering.
  • Incorporating a gradual view refresh technique.

Main Results:

  • The proposed scheme was submitted to the 3DV Call for Proposals (CfP).
  • Achieved an average bitrate reduction of 26% for two-view scenarios and 35% for three-view scenarios compared to the MVC anchor.
  • Subjective tests confirmed similar bitrate reduction, indicating perceptual quality is maintained.

Conclusions:

  • The developed coding scheme offers significant bitrate savings for multiview video plus depth compression.
  • The scheme's compatibility and novel tools provide a viable solution for efficient 3D video coding.
  • The results demonstrate a substantial improvement over existing methods for 3D video data compression.