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Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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 sampling...
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...
Downsampling01:20

Downsampling

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...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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

Convolution: Math, Graphics, and Discrete Signals

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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Related Experiment Videos

Image coding using robust quantization for noisy digital transmission.

Q Chen1, T R Fischer

  • 13Com (US Robotics), Skokie, IL, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 16, 2008
PubMed
Summary
This summary is machine-generated.

A new robust quantizer enhances data encoding and transmission over noisy channels. This system achieves high performance for various sources, even with image coding over a binary symmetric channel.

Related Experiment Videos

Area of Science:

  • Digital Signal Processing
  • Information Theory
  • Image Compression

Background:

  • Robust data encoding is crucial for reliable communication over noisy channels.
  • Channel Optimized Scalar Quantization (COSQ) offers efficient source encoding but can be sensitive to channel errors.
  • Developing quantizers that maintain performance under channel impairments is an ongoing challenge.

Purpose of the Study:

  • To develop a robust quantizer for memoryless sources transmitted over a binary symmetric channel (BSC).
  • To integrate channel-optimized scalar quantization (COSQ) with a novel all-pass filtering technique for enhanced resilience.
  • To evaluate the quantizer's performance across various sources and in image coding applications.

Main Methods:

  • The proposed system combines Channel Optimized Scalar Quantization (COSQ) with a binary phase-scrambling/descrambling method for all-pass filtering.
  • The robust quantizer was designed to encode memoryless sources.
  • Performance was evaluated for a memoryless Gaussian source and in image coding scenarios over a BSC.

Main Results:

  • The robust quantizer achieved performance comparable to the Gaussian COSQ for memoryless Gaussian sources.
  • The system demonstrated graceful degradation of Peak Signal-to-Noise Ratio (PSNR) as the channel bit error rate increased.
  • The quantizer proved effective for image coding applications transmitted over a BSC.

Conclusions:

  • The developed robust quantizer offers a resilient solution for data encoding and transmission over noisy channels.
  • The integration of COSQ with binary phase-scrambling provides a significant improvement in error resilience.
  • This approach maintains high performance, particularly in image coding, even under adverse channel conditions.