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Related Concept Videos

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...
Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
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...
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
Lossless Lines01:23

Lossless Lines

In electrical engineering, a lossless transmission line is characterized by a purely imaginary propagation constant and a resistive characteristic impedance. The ABCD parameters, which describe the relationship between the input and output voltages and currents, indicate an equivalent π circuit with an imaginary series impedance and a shunt admittance. This results in a transmission line that, when the product of the phase constant (beta) and the length of the line is less than pi, exhibits...
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...

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Related Experiment Videos

Preserving step edges in low bit rate progressive image compression.

Dirck Schilling1, Pamela C Cosman

  • 1Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093-0407, USA. dirck.schilling@viasat.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

New image compression algorithms preserve key features like edges in low-bandwidth applications. This improves understanding of images at high compression ratios, crucial for wireless internet access.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Low-bandwidth applications necessitate efficient image transmission.
  • Standard compression methods introduce significant distortion at high compression ratios.
  • Preserving image features is critical for recognition and understanding.

Purpose of the Study:

  • To develop progressive image compression algorithms that maintain clarity of important features.
  • To achieve high compression ratios (80:1 and above) while preserving image quality.
  • To enhance the usability of images in low-bandwidth environments.

Main Methods:

  • Developed two progressive compression algorithms focusing on edge preservation.
  • Algorithm 1: Standard SPIHT (Set Partitioning in Hierarchical Trees) with post-decoder edge enhancement.
  • Algorithm 2: Modified wavelet transform to isolate edges, followed by SPIHT for texture encoding.

Main Results:

  • Both algorithms effectively capture and encode edge locations.
  • Preserved critical image features, such as edges, even at compression ratios exceeding 80:1.
  • Enhanced clarity of encoded edges through specialized post-processing or transform modification.

Conclusions:

  • The proposed algorithms significantly improve image understanding in low-bandwidth scenarios.
  • Edge clarity is well-preserved, aiding image recognition at extreme compression levels.
  • These methods offer a viable solution for transmitting recognizable images over limited bandwidth networks.