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Related Concept Videos

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...
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...
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Related Experiment Videos

Artifact reduction in low bit rate DCT-based image compression.

J Luo1, C W Chen, K J Parker

  • 1Dept. of Electr. Eng., Rochester Univ., NY.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for reducing artifacts in low bit rate discrete-cosine-transform-compressed (DCT-compressed) images. The technique enhances visual quality and peak signal-to-noise ratio (PSNR) for clearer image reconstructions.

Related Experiment Videos

Area of Science:

  • Digital Image Processing
  • Computer Vision
  • Signal Processing

Background:

  • Low bit rate compression introduces significant artifacts in images.
  • Discrete Cosine Transform (DCT) compression is widely used but susceptible to artifacts.
  • Existing artifact reduction methods may not sufficiently improve visual quality or quantitative metrics.

Purpose of the Study:

  • To develop an effective artifact reduction scheme for DCT-compressed images.
  • To improve the visual fidelity and quantitative performance of reconstructed images.
  • To address the challenges of artifact reduction in low bit rate scenarios.

Main Methods:

  • Calibration of DC coefficients using gradient continuity constraints.
  • Application of improved Huber-Markov-Random-Field (HMRF)-based smoothing.
  • Implementation of constrained optimization via Iterative Conditional Mode (ICM).

Main Results:

  • Demonstrated artifact reduction in typical DCT-compressed images.
  • Achieved significant improvements in visual quality.
  • Showcased enhanced Peak Signal-to-Noise Ratio (PSNR) values.

Conclusions:

  • The proposed scheme effectively reduces artifacts in low bit rate DCT-compressed images.
  • The method leads to superior visual quality and quantitative performance.
  • This approach offers a promising solution for enhancing compressed image fidelity.