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Related Concept Videos

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...
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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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...
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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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...
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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...
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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...
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Downsampling01:20

Downsampling

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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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I2C: Invertible Continuous Codec for High-Fidelity Variable-Rate Image Compression.

Shilv Cai, Liqun Chen, Zhijun Zhang

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    Summary
    This summary is machine-generated.

    This study introduces the Invertible Continuous Codec (I2C) for high-fidelity variable-rate image compression. The novel method overcomes limitations of existing approaches, especially after multiple re-encodings, ensuring image fidelity.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Lossy image compression is crucial for media transmission and storage.
    • Variable-rate compression methods are gaining attention but existing Variational Autoencoder (VAE)-based methods suffer from artifacts and fidelity loss after multiple re-encodings.
    • Preserving image fidelity during compression is a significant challenge.

    Purpose of the Study:

    • To address the limitations of existing variable-rate image compression techniques, particularly VAE-based methods, that degrade image fidelity after multiple re-encodings.
    • To propose a novel method for high-fidelity, fine-grained variable-rate image compression.
    • To introduce the Invertible Continuous Codec (I2C) that maintains image quality under continuous re-encoding scenarios.

    Main Methods:

    • Developed the Invertible Continuous Codec (I2C), implementing a mathematically invertible approach using an Invertible Activation Transformation (IAT) module.
    • Built I2C upon a single-rate Invertible Neural Network (INN) model, incorporating a quality level (QLevel) input to the IAT for generating adaptive tensors.
    • Utilized theoretical findings on invertible transformations to preserve image fidelity.

    Main Results:

    • The proposed I2C method significantly outperforms state-of-the-art variable-rate image compression techniques.
    • I2C demonstrates superior performance, especially after multiple continuous re-encodings at different rates, mitigating artifacts and preserving image fidelity.
    • Achieved very fine variable-rate control without compromising compression performance.

    Conclusions:

    • The Invertible Continuous Codec (I2C) offers a robust solution for high-fidelity variable-rate image compression.
    • I2C effectively addresses the fidelity degradation issues encountered by existing methods during multiple re-encodings.
    • The method provides precise rate control and maintains excellent image quality, making it suitable for demanding media applications.