论文标题
加速使用统一分析的GPU编码体系结构加速无损的图像编码
Accelerated Deep Lossless Image Coding with Unified Paralleleized GPU Coding Architecture
论文作者
论文摘要
我们提出了深度无损图像编码（DLIC），这是一种完整的分辨率学习的无损图像压缩算法。我们的算法基于与熵编码器结合的神经网络。神经网络对源图像的每个像素进行密度估计。然后，密度估计用于编码目标像素，以压缩率对FLIF进行击败。已经尝试了类似的方法。但是，长期运行的时间使它们在现实世界应用中不可行。我们引入了基于GPU的实现，允许在不到一秒钟内编码和解码8位图像。由于DLIC使用神经网络来估计用于熵编码器的概率，因此可以在域特定图像数据上训练DLIC。我们通过使用磁铁共振成像（MRI）图像调整和训练DLIC来证明这种能力。
We propose Deep Lossless Image Coding (DLIC), a full resolution learned lossless image compression algorithm. Our algorithm is based on a neural network combined with an entropy encoder. The neural network performs a density estimation on each pixel of the source image. The density estimation is then used to code the target pixel, beating FLIF in terms of compression rate. Similar approaches have been attempted. However, long run times make them unfeasible for real world applications. We introduce a parallelized GPU based implementation, allowing for encoding and decoding of grayscale, 8-bit images in less than one second. Because DLIC uses a neural network to estimate the probabilities used for the entropy coder, DLIC can be trained on domain specific image data. We demonstrate this capability by adapting and training DLIC with Magnet Resonance Imaging (MRI) images.
