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相关实验视频

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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一个轻量级的模型,通过隐性先验来进行感知图像压缩.

Hao Wei1, Yanhui Zhou1, Yiwen Jia1

  • 1Xi'an Jiaotong University, No.28, West Xianning Road, Xi'an, Shaanxi, 710049, China.

Neural networks : the official journal of the International Neural Network Society
|November 13, 2025
PubMed
概括

本研究介绍了一种使用隐式语义先验 (ICISP) 的轻量级感知图像压缩方法. 对于移动设备来说,ICISP在低比特率下实现了具有竞争力的视觉质量,并显著降低了复杂性.

关键词:
频率分解频率分解隐含的语义先验 隐含的语义先验感知图像压缩是一种感知式的图像压缩.视觉状态空间模型的空间模型

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科学领域:

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

背景情况:

  • 与传统方法相比,感知图像压缩在低比特率下提供了优越的视觉质量.
  • 现有的方法通常依赖于明确的语义特征,增加模型复杂性和限制移动应用.

研究的目的:

  • 开发一种轻量级的感知图像压缩方法,可以降低模型复杂性,同时保持高视觉质量.
  • 为了利用隐含的语义先验,在资源有限的设备上进行高效的图像压缩.

主要方法:

  • 提出了一种使用隐含语义先验 (ICISP) 的轻量级感知图像压缩方法.
  • 开发了一个增强的视觉状态空间块,用于利用空间依赖性和减少冗余性.
  • 引入了一个频率分解调制块,用于自适应频率信息保存.
  • 使用语义信息区分器,从DINOv2编码器中隐含先验,以提高感知质量.

主要成果:

  • ICISP 方法在标准基准上证明了具有竞争力的压缩性能.
  • 与最先进的方法相比,实现了显著减少的网络参数和浮点运算.
  • 这种方法有效地平衡了压缩效率和感知质量.

结论:

  • ICISP为移动设备上高质量的感知图像压缩提供了一个实用的解决方案.
  • 隐含的语义先验为减少模型复杂性的显式特征提供了一个有效的替代方案.
  • 提出的方法推进了高效和视觉优化的图像压缩领域.