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English(EN) HEALPix-Based Implicit Neural Compression of Omnidirectional Images

基于HEALPix的压缩提高了全向图像质量

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种使用基于HEALPix的隐式神经表示来压缩全向图像的新颖方法。该方法通过实现等面积采样和高效的层次建模，解决了现有等距柱状投影方法的局限性。提出的球形跨尺度条件熵模型和多环球形卷积通过利用空间和跨尺度相关性来提高编码效率。实验表明，与VVC和基于ERP的编解码器相比，压缩率有了显著提高，并且解码时间明显更快。 AI

影响 这项研究可能为虚拟现实应用中的沉浸式内容带来更高效、更快速的解码。

排序理由 详细介绍全向图像新压缩方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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基于HEALPix的压缩提高了全向图像质量

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详细介绍全向图像新压缩方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yulong Cheng, Youneng Bao, Junfeng Zhou, Mu Li, Jie Wen ·

    基于HEALPix的定向全景图像隐式神经压缩

    arXiv:2610.32561v1 Announce Type: new Abstract: Real-time decoding of omnidirectional images is essential for immersive virtual reality (VR) experiences. Compared with conventional transform-based learned image compression (LIC), overfitted codecs offer lower decoding complexity …

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