基于上下文自适应神经网络的图像压缩预测

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摘要翻译：
本文提出了一种基于全连通神经网络和卷积神经网络的预测神经网络结构，用于图像内预测。用于预测给定图像块的神经网络的选择取决于块的大小，因此不需要向解码器发出信号。结果表明，全连通神经网络对小块具有良好的预测效果，而卷积神经网络对复杂纹理的大块具有更好的预测效果。由于在训练过程中使用了随机大小的掩码，PNN的神经网络很好地适应了可能变化的可用上下文，这取决于要预测的图像块的位置。将PNN集成到H.265编解码器中，可获得1.46%~5.20%的PSNR速率性能增益。这些增益比现有的基于神经网络的方法平均提高了0.99%。与H.265帧内预测模式不同，每种模式都专门用于预测特定的纹理，所提出的PNN可以对大量复杂的纹理进行建模。
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英文标题：
《Context-adaptive neural network based prediction for image compression》
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作者：
Thierry Dumas (Sirocco), Aline Roumy (Sirocco), Christine Guillemot
  (Sirocco)
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最新提交年份：
2019
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  This paper describes a set of neural network architectures, called Prediction Neural Networks Set (PNNS), based on both fully-connected and convolutional neural networks, for intra image prediction. The choice of neural network for predicting a given image block depends on the block size, hence does not need to be signalled to the decoder. It is shown that, while fully-connected neural networks give good performance for small block sizes, convolutional neural networks provide better predictions in large blocks with complex textures. Thanks to the use of masks of random sizes during training, the neural networks of PNNS well adapt to the available context that may vary, depending on the position of the image block to be predicted. When integrating PNNS into a H.265 codec, PSNR-rate performance gains going from 1.46% to 5.20% are obtained. These gains are on average 0.99% larger than those of prior neural network based methods. Unlike the H.265 intra prediction modes, which are each specialized in predicting a specific texture, the proposed PNNS can model a large set of complex textures.
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PDF链接：
https://arxiv.org/pdf/1807.06244

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关键词：神经网络 神经网 上下文 Architecture Construction connected based 图像 predicting PNN