Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...

Bandpass Sampling

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...

Relative Motion Analysis using Rotating Axes

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 Components

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...

You might also read

Related Articles

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

Sort by

Same author

The effect of excess body weight on short-term outcomes following minimally invasive rectal cancer surgery: a systematic review and meta-analysis.

Techniques in coloproctology·2026

Same author

Combining EBV DNA dynamics with RECIST-based radiographic response to improve prognostic stratification of recurrent/metastatic nasopharyngeal carcinoma in the era of immunotherapy.

Rhinology·2026

Same author

[Epidemiological characteristics of female breast cancer in China and worldwide].

Zhonghua zhong liu za zhi [Chinese journal of oncology]·2025

Same author

[Repair strategies and efficacy analysis of deep electrical burn wounds in children's fingers].

Zhonghua shao shang yu chuang mian xiu fu za zhi·2025

Same author

[Experts consensus on sensitized airborne pollen monitoring].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery·2025

Same author

[Spring and summer-autumn pollen grading and forecasting model based on daily visits of allergic rhinitis patients].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery·2025

Search research articles

Related Experiment Videos

Complex-subband transform for subband-based motion estimation/compensation and coding.

C C Lien¹, C L Huang, J G Chen

¹Inst. of Electr. Eng., Nat. Tsing Hua Univ., Hsinchu.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1997

Summary

This summary is machine-generated.

This study introduces the complex subband transform (CST) for image sequence coding. CST-based motion compensation yields higher image quality and more efficient motion vector prediction than conventional methods.

Related Experiment Videos

Area of Science:

Digital image processing
Signal processing
Video compression

Background:

Subband signal decomposition and motion estimation are key processes in image sequence coding.
Conventional block matching motion compensation has limitations in efficiency and reconstructed image quality.

Purpose of the Study:

To introduce the complex subband transform (CST) for enhanced subband-based image sequence coding.
To evaluate the performance of CST in motion estimation and compensation.

Main Methods:

The complex subband transform (CST) was applied for subband signal decomposition.
CST was utilized for motion estimation within the subband framework.
CST-based motion compensation was compared against conventional block matching techniques.

Main Results:

CST-based motion compensation achieved a higher peak signal-to-noise ratio (PSNR) for reconstructed images.
The prediction error entropy of motion vectors was lower using the CST-based approach.
Experimental results demonstrate superior performance of CST over conventional methods.

Conclusions:

The complex subband transform (CST) is an effective method for improving subband-based image sequence coding.
CST enhances both the quality of reconstructed images and the efficiency of motion vector prediction.
This transform offers a promising alternative for advanced video compression techniques.