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Downsampling

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...

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Updated: Jan 15, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Hierarchical Semantic Compression for Consistent Image Semantic Restoration.

Shengxi Li, Zifu Zhang, Mai Xu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|October 13, 2025

Summary

This summary is machine-generated.

This study introduces a novel hierarchical semantic compression (HSC) framework for efficient image compression. The HSC framework achieves state-of-the-art performance in both human and machine vision tasks by operating within intrinsic semantic spaces.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Semantic compression methods face limitations due to pre-defined or high-dimensional semantics, hindering compression efficiency.
Existing approaches struggle with high-fidelity restoration at extremely low bitrates.

Purpose of the Study:

To propose a novel hierarchical semantic compression (HSC) framework for efficient and consistent semantic restoration.
To improve compression efficiency by operating purely within intrinsic semantic spaces of generative models.

Main Methods:

Developed a hierarchical architecture utilizing a general inversion encoder.
Introduced a feature compression network (FCN) and semantic compression network (SCN) for hierarchical compression.
Employed a progressively shared entropy model with channel-wise context for enhanced compression.

Main Results:

The HSC framework achieves state-of-the-art performance in subjective quality and consistency for human vision.
Demonstrated superior performance on machine vision tasks with compressed bitstreams.
Showcased efficient compression with high-fidelity restoration, even at low bitrates.

Conclusions:

The proposed HSC framework offers a new paradigm for image and video compression, aligning with the human visual system's image understanding.
Achieved efficient compression and consistent semantic restoration by leveraging intrinsic semantic spaces.
Provides a foundation for future advancements in image/video compression technologies.