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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...
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...
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...
Trimmed Mean01:10

Trimmed Mean

While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
Maximizing the Directional Derivative01:25

Maximizing the Directional Derivative

The directional derivative is a central concept in multivariable calculus that describes how a function changes at a given point when moving in a specified direction. This direction is represented by a unit vector, ensuring that only the orientation influences the rate of change. By varying the direction, different rates of change can be observed, demonstrating that the directional derivative depends strongly on the chosen direction.The directional derivative is computed using the gradient...
Upsampling01:22

Upsampling

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Related Experiment Videos

Selective data pruning-based compression using high-order edge-directed interpolation.

Dũng T Võ1, Joel Solé, Peng Yin

  • 1Digital Media Solutions Lab, Samsung Information SystemsAmerica, Irvine, CA 92612, USA. dung.vo@samsung.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 6, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a selective data pruning method for image and video compression, enhancing the rate-distortion performance. The technique prunes data before compression and interpolates it post-decoding, minimizing distortion for better quality.

Related Experiment Videos

Area of Science:

  • Digital image processing
  • Video compression algorithms
  • Data compression techniques

Background:

  • Improving the rate-distortion performance is crucial for efficient image and video compression.
  • Existing methods may struggle with maintaining quality after compression and decompression.

Purpose of the Study:

  • To propose a selective data pruning-based compression scheme.
  • To enhance the rate-distortion relation for compressed images and video sequences.
  • To minimize distortion during interpolation after decompression.

Main Methods:

  • Selective data pruning of original frames before compression.
  • Edge-directed interpolation to restore frames to original size post-decoding.
  • Optimization of data pruning for minimal interpolation distortion.
  • Novel high-order interpolation adapting to edge directions.
  • Multiframe-based interpolation using spatio-temporal pixels.

Main Results:

  • The proposed method improves the rate-distortion performance.
  • The novel high-order interpolation is more robust than existing fourth-order methods.
  • Simulation results validate the effectiveness for image interpolation and coding.

Conclusions:

  • Selective data pruning combined with advanced interpolation offers superior image and video compression.
  • The high-order and multiframe interpolation techniques effectively reduce distortion and enhance quality.