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Related Concept Videos

Upsampling01:22

Upsampling

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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...
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Downsampling01:20

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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.
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Uniform Depth Channel Flow

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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...
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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...
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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.
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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.
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Related Experiment Video

Updated: Mar 20, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Multiview video plus depth transmission via virtual-view-assisted complementary down/upsampling.

Zhi Jin1, Tammam Tillo2, Jimin Xiao2

  • 1Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Ren Ai Road 111, Suzhou, 215123 People's Republic of China ; Department of Electrical Engineering and Electronics, University of Liverpool, Merseyside L69 3BX, Liverpool, UK.

EURASIP Journal on Image and Video Processing
|May 28, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel down/upsampling algorithm for multiview video plus depth (MVD) to reduce data transmission. The method effectively recovers high-frequency details and enhances edge recovery at the decoder.

Keywords:
DIBRDepth mapLow bit rate video transmissionMultiview video plus depth (MVD)Video codingVirtual view

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Area of Science:

  • Computer Science
  • Image Processing
  • Video Compression

Background:

  • Multiview video plus depth (MVD) offers immersive 3D experiences but demands high computational complexity and bandwidth.
  • Existing mixed-resolution video techniques reduce data but can compromise quality.
  • Efficiently transmitting MVD while maintaining visual fidelity remains a challenge.

Purpose of the Study:

  • To propose an efficient down/upsampling algorithm for MVD that minimizes data transmission.
  • To enhance the quality of reconstructed video at the decoder side.
  • To systematically consider both encoder and decoder operations for optimal performance.

Main Methods:

  • A novel down/upsampling algorithm is proposed for MVD.
  • At the encoder, adjacent view rows are downsampled using an interlacing and complementary approach.
  • At the decoder, discarded pixels are recovered by fusing virtual view pixels with directional interpolated pixels, utilizing surrounding texture patterns for edge enhancement.

Main Results:

  • The proposed algorithm effectively recovers discarded high-frequency details using virtual views.
  • Edge recovery is enhanced through texture-guided data fusion.
  • Experimental results demonstrate the superior performance of the proposed framework compared to existing methods.

Conclusions:

  • The developed down/upsampling algorithm significantly reduces data requirements for MVD transmission.
  • The approach successfully maintains and enhances video quality at the decoder.
  • This method offers a promising solution for efficient and high-quality MVD delivery.