论文标题
具有卷积神经网络预测的镶嵌图像的无损压缩
Lossless Compression of Mosaic Images with Convolutional Neural Network Prediction
论文作者
论文摘要
我们为数码相机的原色镶嵌图像提供了基于CNN的预测无损压缩方案。此专门的应用问题以前已经研究了，但是现在变得越来越重要，因为现代的CNN方法用于图像恢复任务（例如，超分辨率，低照明增强，脱脂）必须在原始的原始镶嵌图像上运行，以获得最佳的结果。本文的关键创新是空间光谱镶嵌模式的高级非线性CNN预测指标。深度学习预测可以更准确地对空间 - 光谱镶嵌图像中高度复杂的样本依赖性进行建模，因此比现有图像预测指标更彻底地删除统计冗余。实验表明，所提出的CNN预测器在摄像机原始图像上实现了前所未有的无损压缩性能。
We present a CNN-based predictive lossless compression scheme for raw color mosaic images of digital cameras. This specialized application problem was previously understudied but it is now becoming increasingly important, because modern CNN methods for image restoration tasks (e.g., superresolution, low lighting enhancement, deblurring), must operate on original raw mosaic images to obtain the best possible results. The key innovation of this paper is a high-order nonlinear CNN predictor of spatial-spectral mosaic patterns. The deep learning prediction can model highly complex sample dependencies in spatial-spectral mosaic images more accurately and hence remove statistical redundancies more thoroughly than existing image predictors. Experiments show that the proposed CNN predictor achieves unprecedented lossless compression performance on camera raw images.
