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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
Downsampling01:20

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...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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Improving the performance of 3D image model compression based on optimized DEFLATE algorithm.

Xue Kai1, Zhang Yuxiang2

  • 1School of Humanities and Social Science, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China. xuekai@stu.xjtu.edu.cn.

Scientific Reports
|June 28, 2024
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Summary

The DEFLATE algorithm offers superior compression for 3D image models, especially smaller ones, reducing compression time and enhancing data transmission reliability. It

Keywords:
3D image model compressionCompression ratioCompression timeDEFLATE algorithmPerformance optimization

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Area of Science:

  • Computer Science
  • Data Compression
  • 3D Imaging

Background:

  • The DEFLATE algorithm, a combination of LZ77 and Huffman coding, is widely used for data compression.
  • Optimizing DEFLATE for NX 3D image models is crucial for efficient data handling.
  • Existing compression methods like Wavelet and C-Bone algorithms serve as benchmarks.

Purpose of the Study:

  • To optimize and design the DEFLATE algorithm for enhanced compression performance and reduced compression time.
  • To evaluate DEFLATE's effectiveness in compressing NX 3D image models of varying sizes.
  • To compare DEFLATE against Wavelet and C-Bone algorithms in terms of compression ratio and time.

Main Methods:

  • Selected three NX 3D image models of different sizes for experimentation.
  • Applied DEFLATE, Wavelet, and C-Bone algorithms for compression.
  • Analyzed compression ratio, compression time, and data transmission reliability.

Main Results:

  • DEFLATE achieved significantly higher compression ratios and shorter compression times for small and medium 3D models compared to Wavelet and C-Bone.
  • DEFLATE improved 3D image model compression performance by 15% and data throughput by 49% over the Wavelet algorithm.
  • For large models, DEFLATE offered comparable compression ratios but reduced compression time and enhanced transmission reliability by 12.1% over Wavelet.

Conclusions:

  • The DEFLATE algorithm is an excellent choice for compressing 3D image models, particularly small and medium-sized ones.
  • DEFLATE provides practical benefits for large 3D model compression due to reduced time and improved reliability.
  • This study provides valuable insights for DEFLATE optimization and future 3D image compression research.