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Related Experiment Videos

A fast and efficient adaptive threshold rate control scheme for remote sensing images.

Xiao Chen1, Xiaoqing Xu

  • 1Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China. rainofsun@netease.com

Thescientificworldjournal
|December 6, 2012
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

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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This study introduces an improved JPEG2000 compression algorithm for remote sensing images. It significantly reduces computational cost and memory usage while maintaining high image quality.

Area of Science:

  • Remote Sensing
  • Image Compression
  • Computer Vision

Background:

  • JPEG2000 is suitable for remote sensing but computationally intensive.
  • Limited resources in remote sensing hinder JPEG2000 adoption.
  • Existing algorithms face challenges with complexity and memory demands.

Purpose of the Study:

  • To develop an efficient rate control algorithm for JPEG2000 in remote sensing.
  • To reduce computational complexity and memory buffer requirements.
  • To maintain high image quality with optimized compression.

Main Methods:

  • Implemented a novel rate control algorithm for JPEG2000.
  • Sorted coded blocks by bit planes before entropy coding.
  • Utilized an adaptive threshold for truncating code block passes during Tier-1 encoding.

Related Experiment Videos

Main Results:

  • Reduced computational cost to 18.13% of prior methods.
  • Decreased working buffer memory size to 7.81% of prior methods.
  • Achieved comparable Peak Signal-to-Noise Ratio (PSNR) to existing algorithms.

Conclusions:

  • The proposed algorithm significantly enhances coding efficiency for remote sensing images.
  • It drastically lowers computational and memory demands.
  • Image quality is preserved, making it suitable for resource-constrained remote sensing applications.