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相关概念视频

Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

318
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
318
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

251
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
251
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

203
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...
203
Upsampling01:22

Upsampling

238
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...
238
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

262
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...
262
Downsampling01:20

Downsampling

158
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...
158

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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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I2C:用于高保真度可变速率图像压缩的可逆连续编解码器.

Shilv Cai, Liqun Chen, Zhijun Zhang

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    概括
    此摘要是机器生成的。

    本研究介绍了用于高保真度可变速率图像压缩的可逆连续编码器 (I2C). 这种新的方法克服了现有方法的局限性,特别是在多次重新编码后,确保了图像保真性.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 图像处理 图像处理
    • 机器学习 机器学习

    背景情况:

    • 丢失的图像压缩对于媒体传输和存储至关重要.
    • 可变速率压缩方法正在引起人们的注意,但现有的基于变量自编码器 (VAE) 的方法在多次重新编码后会出现工件和保真性损失.
    • 在压缩过程中保持图像保真是一个重大挑战.

    研究的目的:

    • 解决现有的变速图像压缩技术的局限性,特别是基于VAE的方法,在多次重新编码后降低图像保真度.
    • 提出一种用于高保真,细粒度变速图像压缩的新方法.
    • 引入可逆连续编码器 (I2C),在连续重新编码场景下保持图像质量.

    主要方法:

    • 开发了可逆连续编解码器 (I2C),使用可逆激活转换 (IAT) 模块实现了数学可逆方法.
    • 基于单速率可逆神经网络 (INN) 模型构建的I2C,将质量级 (QLevel) 输入到IAT以生成自适应张量.
    • 利用关于可逆变换的理论发现来保持图像保真.

    主要成果:

    • 拟议的I2C方法显著优于最先进的可变速率图像压缩技术.
    • I2C表现出卓越的性能,特别是在不同速率的多次连续重新编码后,减轻了工件并保持了图像保真度.
    • 在不影响压缩性能的情况下实现了非常精细的可变速率控制.

    结论:

    • 可逆连续编解码器 (I2C) 为高保真性可变速率图像压缩提供了强大的解决方案.
    • I2C有效地解决了现有方法在多次重新编码时遇到的忠实度降低问题.
    • 该方法提供精确的速率控制,并保持出色的图像质量,使其适用于苛刻的媒体应用.