Jove
Visualize
Contact Us

Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it instrumental in...
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the time...
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the drone...
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...
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...

You might also read

Related Articles

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

Sort by
Same author

A new compression strategy to reduce the size of nanopore sequencing data.

Genome research·2025
Same author

Exploration of Learned Lifting-Based Transform Structures for Fully Scalable and Accessible Wavelet-Like Image Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2024
Same author

JPEG 2000 Extensions for Scalable Coding of Discontinuous Media.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023
Same author

Transform Quantization for CNN Compression.

IEEE transactions on pattern analysis and machine intelligence·2021
Same author

Gaussian Lifting for Fast Bilateral and Nonlocal Means Filtering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2020
Same author

Fast Optical Flow Extraction from Compressed Video.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2020
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 Experiment Videos

Lifting-based invertible motion adaptive transform (LIMAT) framework for highly scalable video compression.

Andrew Secker1, David Taubman

  • 1School of Electrical Engineering, The University of New South Wales, Sydney 2052, Australia.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

We introduce a new scalable video compression framework using a lifting-based invertible motion adaptive transform (LIMAT). This method enables high coding gains and visually pleasing reconstructions, advancing scalable video compression techniques.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Signal Processing
  • Information Theory

Background:

  • Scalable video compression is crucial for efficient transmission across diverse networks.
  • Existing methods face limitations due to invertibility constraints with complex motion compensation.
  • A need exists for advanced frameworks supporting fine-grained scalability and high coding efficiency.

Purpose of the Study:

  • To propose a novel framework for highly scalable video compression.
  • To overcome limitations of previous approaches by preserving invertibility with arbitrary motion models.
  • To demonstrate the effectiveness of advanced motion modeling and temporal wavelet transforms.

Main Methods:

  • Development of a lifting-based invertible motion adaptive transform (LIMAT).
  • Implementation of temporal wavelet transform along motion trajectories using motion-compensated lifting steps.
  • Utilizing complex motion modeling, such as deformable triangular meshes.

Main Results:

  • Achieved high coding gain through a finely embedded, scalable compressed bit-stream.
  • Demonstrated the effectiveness of non-Haar temporal wavelet kernels and complex motion modeling.
  • Obtained visually pleasing video reconstructions at reduced frame rates.

Conclusions:

  • The proposed LIMAT framework offers a new paradigm for highly scalable video compression.
  • It effectively integrates temporal wavelet transforms with complex motion compensation while maintaining invertibility.
  • The framework shows significant advantages over previous strategies for scalable video coding.