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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

631
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.
631
Upsampling01:22

Upsampling

230
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...
230
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

63
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...
63
Uniform Depth Channel Flow01:27

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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Downsampling01:20

Downsampling

154
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...
154
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

248
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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LSSVC:一个学习的空间可扩展的视频编码方案.

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    概括
    此摘要是机器生成的。

    本研究介绍了LSSVC,这是一个新的学习空间可扩展的视频编码方案. 它通过利用基层信息来提高增强层的压缩性能,显著提高了压缩性能,超过了现有的标准.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 视频压缩 视频压缩
    • 机器学习 机器学习

    背景情况:

    • 传统的基于区块的视频编码在压缩效率方面存在局限性.
    • 学习视频编码显示有望提高性能.
    • 现有的可扩展的视频编码方法对进一步增长的空间有限.

    研究的目的:

    • 提出一个端到端学习的空间可扩展视频编码 (LSSVC) 方案.
    • 为了提高可扩展视频编码中的压缩性能.
    • 提供一种利用基层信息的新型解决方案.

    主要方法:

    • 开发了一个端到端学习的空间可扩展视频编码方案 (LSSVC).
    • 利用基层 (BL) 运动,纹理和潜在信息作为增强层 (EL) 的间层信息.
    • 设计了三个模块:上下文运动向量 (MV) 编码器-解码器,混合时层上下文挖掘,以及具有BL潜伏先验的模型.

    主要成果:

    • 与H.265/SHVC相比,LSSVC表现出优越的压缩性能.
    • 拟议的模块有效地减少了层间冗余.
    • 利用BL信息改善了高分辨率MV和潜伏压缩.

    结论:

    • 在学习空间可扩展视频编码方面,LSSVC提供了显著的进步.
    • 整合BL信息对于增强EL压缩至关重要.
    • 拟议的方案为可扩展的视频压缩提供了一个新的最先进的解决方案.