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Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
The Entropy as a State Function01:14

The Entropy as a State Function

Consider an arbitrary process that moves between two specific states (A and B) in a cyclic manner. This process is reversible and broken down into smaller parts that each follow a Carnot cycle. A Carnot cycle has two isothermal (constant temperature) processes. During these processes, the ratio of the amount of heat transferred to their respective temperature remains constant. The other two processes in the Carnot cycle are also reversible but adiabatic, which means they occur without any heat...
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
Entropy and the Second Law of Thermodynamics01:26

Entropy and the Second Law of Thermodynamics

Consider an isolated system in which a hot object is placed in contact with a cold one. This is an irreversible process that eventually leads both objects to reach the same equilibrium temperature. It is crucial to note that the constituents of any substance exhibit increased disorder at higher temperatures. As a cold substance absorbs heat, its constituents become more disordered. The energy transfer from a hotter object to a cooler one increases the system's disorder or randomness. This...
Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

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Related Experiment Videos

Symmetric Entropy-Constrained Video Coding for Machines.

Yuxiao Sun, Meiqin Liu, Chao Yao

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 24, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel Symmetric Entropy-Constrained Video Coding for Machines (SEC-VCM) framework. SEC-VCM enhances video coding for machine vision by aligning codecs with visual backbones, achieving significant bitrate savings and state-of-the-art performance.

    Related Experiment Videos

    Area of Science:

    • Computer Vision
    • Video Compression
    • Machine Learning

    Background:

    • Video transmission increasingly serves machine vision systems (MVS), necessitating specialized video coding for machines (VCM).
    • Existing VCM methods often lack generalization due to task-specific codec binding and reliance on retraining or supervised data.
    • Unified VCM frameworks using visual backbones (VB) and visual foundation models (VFM) show promise but seldom directly link coding with understanding.

    Purpose of the Study:

    • To propose a novel Symmetric Entropy-Constrained Video Coding framework for Machines (SEC-VCM).
    • To establish a symmetric alignment between video codecs and VBs for improved semantic preservation and irrelevant information discarding.
    • To enhance machine-oriented reconstruction quality by suppressing artifacts and injecting pixel-level priors.

    Main Methods:

    • Developed a Symmetric Entropy-Constrained Video Coding framework for Machines (SEC-VCM).
    • Implemented a bi-directional entropy-constraint (BiEC) mechanism for symmetric alignment between video decoding and VB encoding.
    • Introduced a semantic-pixel dual-path fusion (SPDF) module for integrating pixel-level priors and semantic information.

    Main Results:

    • Achieved state-of-the-art (SOTA) rate-task performance on classical video understanding and MLLM-based tasks.
    • Demonstrated significant bitrate savings compared to H.266/VVC reference software VTM across various tasks.
    • Reported average bitrate savings of 37.4% for video instance segmentation, 29.8% for video object segmentation, 46.2% for object detection, 44.9% for multiple object tracking, and 97.6% for MLLM-based video grounding.

    Conclusions:

    • The proposed SEC-VCM framework effectively leverages VB capabilities for semantic preservation and irrelevant information suppression in VCM.
    • The BiEC mechanism and SPDF module contribute to improved reconstruction quality and significant bitrate reduction for machine vision applications.
    • SEC-VCM offers a generalized and efficient solution for video coding tailored to machine vision systems.