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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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Image Compression Based on Hybrid Domain Attention and Postprocessing Enhancement.

Yuting Bao1, Yuwen Tao1, Pengjiang Qian1

  • 1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.

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Summary

This study introduces a novel deep learning image compression method using hybrid domain attention and postprocessing. It enhances compression efficiency by improving entropy estimation and reducing latent representation redundancy.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Signal Processing

Background:

  • Deep learning-based image compression relies on entropy models and encoder-decoder networks.
  • Inaccurate entropy estimation and redundant latent representations limit compression efficiency.

Purpose of the Study:

  • To propose an advanced image compression method using a hybrid domain attention mechanism and postprocessing.
  • To enhance compression efficiency by improving entropy estimation and latent feature representation.

Main Methods:

  • Embedding hybrid domain attention modules in encoder-decoder and hyperprior networks.
  • Modeling latent features with parametric Gaussian-scale mixture models for precise entropy estimation.
  • Incorporating inverse quantization and postprocessing enhancement modules for improved performance.

Main Results:

  • The proposed method achieves higher Peak Signal-to-Noise Ratio (PSNR) and Multiscale Structural Similarity (MS-SSIM) compared to existing methods.
  • Demonstrated superior compression performance over traditional and advanced neural network-based approaches.

Conclusions:

  • The hybrid domain attention mechanism effectively creates compact latent features and hyperpriors.
  • The proposed method significantly improves image compression efficiency and quality through enhanced entropy modeling and postprocessing.