为了倡导基于深度学习的视频编码研究,本文对我们开发的视频编解码器即深度学习视频编码(Deep Learning Video Coding,DLVC)进行了案例研究。DLVC具有两个深度工具,分别为基于CNN的环路滤波器(CNN-based in-loop filter,CNN-ILF)以及基于CNN的块自适应分辨率编码(CNN-based block adaptive resolution coding,CNN-BARC)。
Video communication systemsMachine learningDeep learningArtificial neural networksAvailability is one of the primary goals of smart networks, especially, if the network is under heavy video streaming traffic. In this paper, we propose a deep learning based methodology to enhance availability of video ...
2.2 Deep learning-based video coding Recently, deep learning (a branch of artificial intelligence) has seen great success in computer vision tasks [24, 25], especially for video encoding [26,27,28]. Indeed, deep neural networks have been adopted to improve coding tools, including intra- and ...
The past decade has witnessed great success of deep learning technology in many disciplines, especially in computer vision and image processing. However, deep learning-based video coding remains in its infancy. This paper reviews the representative works about using deep learning for image/video coding...
II. SYSTEM ARCHITECTURE OF NEURAL NETWORK BASED VIDEO COMPRESSION: 本节介绍我们的深度视频编码器的系统结构,如图1所示。利用帧内相关或帧间相关通过预测编码器形成图像块的紧凑表征预测,并利用帧间/帧内残差网络对残差进行压缩。预测系数和残差系数都经过量化和熵编码,生成最终的二进制流。如图1所示,整个编码系统包...
Loss during NoGAN learning is two parts: One is a basic Perceptual Loss (or Feature Loss) based on VGG16 – this just biases the generator model to replicate the input image. The second is the loss score from the critic. For the curious – Perceptual Loss isn't sufficient by itself to...
Deep Learning/Deep neural network-based Image/Video (Quantized) Compressed/Compressive Sensing (Coding)
We present a new algorithm for video coding, learned end-to-end for the low-latency mode. In this setting, our approach outperforms all existing video codecs across nearly the entire bitrate range. To our knowledge, this is the first ML-based method to do so. ...
in-depth learning became essential for machine learning practitioners and even for many software engineers. This book provides a wide range of role for data scientists and software engineers with experience in machine learning. You will start with the basics of deep learning and quickly move on to...
This is a deep learning based model. More specifically, what I've done is combined the following approaches: Self-Attention Generative Adversarial Network (https://arxiv.org/abs/1805.08318). Except the generator is a pretrained U-Net, and I've just modified it to have the spectral normalizati...