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Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

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Related Experiment Video

Updated: May 22, 2026

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes
06:25

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Published on: February 23, 2024

Data compression by shape compensation for mobile video sensors.

Ben-Shung Chow1

  • 1Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan, 80424, ROC; E-Mail: bschow@mail.ee.nsysu.edu.tw ; Tel. +886-07-525-2000 (Ext. 4172);

Sensors (Basel, Switzerland)
|May 11, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a low-cost binary video compression method for mobile communication. Shape compensation effectively reduces transmission and computation costs in low-resolution video for inexpensive sensors.

Keywords:
Camera sensorLow resolutionMobile video

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Last Updated: May 22, 2026

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes
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Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Published on: February 23, 2024

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study
07:51

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

Published on: March 14, 2017

Area of Science:

  • Computer Vision
  • Signal Processing
  • Video Compression

Background:

  • Traditional security systems rely heavily on automatic recognition, demanding significant bandwidth and computing power.
  • Certain applications like baby monitors and mobile intruder avoidance can benefit from reduced computational and transmission costs.
  • Existing methods often involve complex transformations like Discrete Cosine Transformation (DCT).

Purpose of the Study:

  • To propose a novel binary video compression method for low-cost mobile video communication.
  • To reduce transmission bandwidth and computation costs for inexpensive camera sensors.
  • To offer an alternative to standard compression techniques in specific low-resource scenarios.

Main Methods:

  • Development of a binary video compression technique operating at low resolutions.
  • Implementation of shape compensation as a replacement for standard Discrete Cosine Transformation (DCT).
  • Integration of motion compensation within the compression pipeline.

Main Results:

  • The proposed binary video compression method achieves low-cost mobile video communication.
  • Shape compensation effectively replaces Discrete Cosine Transformation (DCT) after motion compensation.
  • The technique is suitable for inexpensive camera sensors and low-resolution video.

Conclusions:

  • The binary video compression method offers a cost-effective solution for mobile video applications.
  • Shape compensation is a viable and efficient alternative to DCT in this context.
  • This approach enables low-cost video communication for devices with limited resources.