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Scaling

Scaling

In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...

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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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Related Experiment Video

Updated: Sep 29, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Scalable Image Coding for Humans and Machines.

Hyomin Choi, Ivan V Bajic

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

|March 24, 2022

Summary

This summary is machine-generated.

This study introduces a novel scalable image codec for machine vision tasks. The developed codec achieves significant bitrate savings for automated visual analytics while maintaining high-quality reconstruction for human viewing.

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Area of Science:

Computer Vision
Image Compression
Machine Learning

Background:

A growing volume of visual content is processed by machines, not humans, for tasks like surveillance and autonomous navigation.
Existing image codecs are not optimized for machine vision analytics, leading to inefficiencies.
Scalability in image representation is crucial for adapting to diverse machine vision task complexities.

Purpose of the Study:

To develop an end-to-end learned image codec with a scalable latent space.
To support both human viewing and diverse machine vision analytics.
To evaluate the codec's performance against benchmarks for bitrate savings and reconstruction quality.

Main Methods:

Designed an image codec with a layered latent space (base and enhancement layers).
Implemented 2-layer and 3-layer models for experimental validation.
Compared performance against state-of-the-art codecs on machine vision tasks and input reconstruction.

Main Results:

Achieved 37%-80% bitrate savings for machine vision tasks compared to existing alternatives.
Demonstrated comparable input reconstruction quality to state-of-the-art image codecs.
The scalable architecture effectively supports varying task complexities.

Conclusions:

The proposed scalable image codec offers significant efficiency gains for machine vision applications.
The codec provides a flexible solution for future visual content analysis.
This approach bridges the gap between human-centric and machine-centric image compression.