Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Reducing Line Loss01:18

Reducing Line Loss

193
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...
193
Upsampling01:22

Upsampling

309
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...
309
Downsampling01:20

Downsampling

252
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...
252
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

252
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
252
Computed Tomography01:10

Computed Tomography

6.2K
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...
6.2K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

50
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...
50

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

HANeRV: Hierarchically Adaptive Neural Representation for Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

RCodSpace: A Robust Learned Coding Method for Deep Space Visual Transmission.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Antibody-antigen neutralization prediction by integrating structural information distillation and physicochemical constraints.

Briefings in bioinformatics·2026
Same author

Outstanding 1200 °C Oxidation Resistance in a Novel Multi-Principal Element Alloy via Lattice Distortion-Induced Diffusion Suppression.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Post-Processing Geometry Enhancement for G-PCC Compressed LiDAR via Cylindrical Densification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Multimodal therapy for primary ureteral small cell neuroendocrine carcinoma with high-grade urothelial component: case report and literature review.

Frontiers in urology·2025
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Ver todos los artículos relacionados

Video Experimental Relacionado

Updated: Sep 10, 2025

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

676

Marco optimizado de velocidad-distorsión-complejidad para la compresión de imágenes de varios modelos

Xinyu Hang, Ziqing Ge, Hongfei Fan

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |August 21, 2025
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Este estudio introduce un nuevo marco de codificación de imágenes multi-modelo para la compresión de imágenes aprendidas. Asigna dinámicamente códecs para optimizar la calidad y la velocidad, reduciendo significativamente el tiempo de decodificación.

    Más Videos Relacionados

    Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain
    05:49

    Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain

    Published on: July 14, 2023

    1.5K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.0K

    Videos de Experimentos Relacionados

    Last Updated: Sep 10, 2025

    Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
    02:09

    Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

    Published on: April 12, 2024

    676
    Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain
    05:49

    Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain

    Published on: July 14, 2023

    1.5K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.0K

    Área de la Ciencia:

    • Visión por computadora
    • Aprendizaje automático
    • Procesamiento de imágenes

    Sus antecedentes:

    • Los marcos de compresión de imágenes aprendidas (LIC) se enfrentan a desafíos con aplicación universal debido a diversos diseños de modelos y datos de capacitación.
    • Un solo modelo de codificación se esfuerza por adaptarse a la amplia variabilidad de las características de la imagen y los requisitos de compresión.

    Objetivo del estudio:

    • Desarrollar un marco pionero de codificación de imágenes multi-modelo para la compresión de imágenes aprendidas.
    • Optimizar el equilibrio entre velocidad, distorsión y complejidad mediante la asignación dinámica de códecs de imagen a diferentes regiones de imagen.
    • Mejorar la calidad de la reconstrucción bajo restricciones de tiempo de bitrate y decodificación.

    Principales métodos:

    • Integración de diversos códecs de imagen en un marco unificado.
    • Algoritmo de asignación de códec dinámico que utiliza el recocido simulado (SA) para la optimización.
    • Aplicación de una estrategia de reducción de las emisiones para mejorar la eficiencia.
    • Asegurar la compatibilidad con los códecs de imagen estándar sin modificaciones estructurales.

    Principales resultados:

    • Se logró una reducción significativa del 70% en el tiempo de decodificación en comparación con los métodos más avanzados.
    • Estableció un nuevo estándar en LIC, avanzando en la frontera de Pareto para las compensaciones de rendimiento y complejidad.
    • Superó a los códecs optimizados para velocidad de distorsión y complejidad (RDC) existentes, con velocidades de decodificación hasta 30 veces más rápidas.
    • Mantuvo la calidad de la reconstrucción sin comprometerla.

    Conclusiones:

    • El marco multimodelo propuesto ofrece una solución de alto rendimiento y ubicua para la compresión de imágenes aprendidas.
    • La asignación dinámica de códec aborda efectivamente las limitaciones de los enfoques de un solo modelo.
    • El marco mejora significativamente la eficiencia y la velocidad de decodificación al tiempo que preserva la calidad de la imagen.