Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Downsampling01:20

Downsampling

605
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...
605

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Joint effects of severe obesity and inflammation on mortality in critically ill non-ST-segment elevation myocardial infarction patients: a cohort study with external validation.

Frontiers in endocrinology·2026
Same author

LKCAU-Net: A Large Kernel Coordinated Attention U-Net for Breast Tumors Segmentation in Ultrasound Images.

Ultrasonic imaging·2026
Same author

Valence-activated arsenic nanozyme enables cascade-amplified chemodynamic therapy for hepatocellular carcinoma.

International journal of pharmaceutics·2026
Same author

Hyperplasia suppressor gene inhibits the progression of malignant meningioma via the Wnt/β-catenin signaling pathway.

Translational cancer research·2026
Same author

A teleost-specific oxygen-immunity axis where FIH activates NF-κB via competitive IκBα binding.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Prognostic Value of the Geriatric Nutritional Risk Index for Long-Term All-Cause Mortality in Older Patients with Non-ST-Segment Elevation Myocardial Infarction.

Clinical nutrition (Edinburgh, Scotland)·2026

相关实验视频

Updated: Jan 15, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

735

层次语义压缩用于一致的图像语义恢复

Shengxi Li, Zifu Zhang, Mai Xu

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |October 13, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了一种新的层次语义压缩 (HSC) 框架,用于高效的图像压缩. 通过在内在的语义空间中运行,HSC框架在人类和机器视觉任务中实现了最先进的性能.

    更多相关视频

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.3K

    相关实验视频

    Last Updated: Jan 15, 2026

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    735
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.3K

    科学领域:

    • 计算机视觉 计算机视觉
    • 图像处理 图像处理
    • 机器学习 机器学习

    背景情况:

    • 语义压缩方法由于预定义或高维语义而面临限制,阻碍了压缩效率.
    • 现有的方法在极低比特率的高保真恢复方面扎.

    研究的目的:

    • 提出一种新的层次语义压缩 (HSC) 框架,以实现高效和一致的语义恢复.
    • 通过纯粹在生成模型的内在语义空间内运行来提高压缩效率.

    主要方法:

    • 开发了一个使用通用反转编码器的等级架构.
    • 引入了特征压缩网络 (FCN) 和语义压缩网络 (SCN) 用于层次压缩.
    • 采用渐进共享模型与通道智能上下文用于增强压缩.

    主要成果:

    • 高清视觉系统 (HSC) 框架在主观质量和人类视觉的一致性方面实现了最先进的性能.
    • 在使用压缩比特流的机器视觉任务中表现出卓越的性能.
    • 展示了高效的压缩与高保真恢复,即使在低比特率.

    结论:

    • 拟议的HSC框架为图像和视频压缩提供了一个新的范式,与人类视觉系统的图像理解保持一致.
    • 通过利用内在的语义空间,实现了高效的压缩和一致的语义恢复.
    • 为图像/视频压缩技术的未来进步提供了基础.