Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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...
Lossy Lines and Overvoltages01:22

Lossy Lines and Overvoltages

Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
When constant series resistance and shunt conductance are present, voltage and current equations are modified. The propagation constant indicates that voltage and current waves consist of both forward and backward traveling components. These waves attenuate as they propagate, with the attenuation factor related to the resistance and conductance. In a...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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...
Lossless Lines01:23

Lossless Lines

In electrical engineering, a lossless transmission line is characterized by a purely imaginary propagation constant and a resistive characteristic impedance. The ABCD parameters, which describe the relationship between the input and output voltages and currents, indicate an equivalent π circuit with an imaginary series impedance and a shunt admittance. This results in a transmission line that, when the product of the phase constant (beta) and the length of the line is less than pi, exhibits...
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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A full-length cDNA of hREV3 is predicted to encode DNA polymerase zeta for damage-induced mutagenesis in humans.

Mutation research·1999
Same author

A corepressor and chicken ovalbumin upstream promoter transcriptional factor proteins modulate peroxisome proliferator-activated receptor-gamma2/retinoid X receptor alpha-activated transcription from the murine lipoprotein lipase promoter.

Endocrinology·1999
Same author

Pharmacomechanical coupling: the role of calcium, G-proteins, kinases and phosphatases.

Reviews of physiology, biochemistry and pharmacology·1999
Same author

Classification terms in developmental toxicology: need for harmonisation. Report of the Second Workshop on the Terminology in Developmental Toxicology Berlin, 27-28 August 1998.

Reproductive toxicology (Elmsford, N.Y.)·1999
Same author

The pioneer gene, apontic, is required for morphogenesis and function of the Drosophila heart.

Mechanisms of development·1999
Same author

Mutations of OCTN2, an organic cation/carnitine transporter, lead to deficient cellular carnitine uptake in primary carnitine deficiency.

Human molecular genetics·1999

Related Experiment Videos

Lossless compression of continuous-tone images via context selection, quantization, and modeling.

X Wu1

  • 1Dept. of Comput. Sci., Univ. of Western Ontario, London, Ont.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
Summary

This study introduces new context modeling techniques for lossless image compression, addressing computational costs and context dilution. The developed methods improve compression performance while maintaining practicality for continuous-tone images.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Image Processing
  • Data Compression

Background:

  • Context modeling is crucial for lossless image compression.
  • High-order Markovian modeling faces challenges in computational cost and context dilution.
  • Existing methods struggle with practical implementation for continuous-tone images.

Purpose of the Study:

  • To develop novel context modeling techniques for lossless compression of continuous-tone images.
  • To overcome the computational and spatial complexity issues of high-order Markovian models.
  • To mitigate the problem of context dilution in image compression.

Main Methods:

  • Exploiting context-dependent DPCM (Differential Pulse Code Modulation) error structures.
  • Developing algorithmic techniques for forming and quantizing modeling contexts.
  • Implementing innovative context formation, quantization, and usage strategies.

Main Results:

  • Achieved highly competitive compression performance for lossless image coding.
  • Significantly reduced computational time and space complexities.
  • Alleviated the issue of context dilution through advanced modeling techniques.

Conclusions:

  • The proposed context modeling approach offers a practical and efficient solution for lossless image compression.
  • The new techniques effectively balance compression performance with computational feasibility.
  • This work advances the state-of-the-art in practical lossless image compression algorithms.