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)。这两种工具都有助于显著提高压缩效率。在随机存取和低延迟配置下,利用这两种深度工具以及其他非深度编码工具,DLVC比HEVC平均节省39.6%和33...
With the development of video technologies, the video representations are extended with the following five trends, as shown in Fig. 2. 1) spatial resolution (x,y): the spatial resolution of video (x,y) grows continuously to enhance the video clarity. It is from the Common Intermediate Format...
In this paper, we aim to provide a comprehensive overview on machine learning based video coding optimization. The main contributions of this work are: 1) We summarize the representations and redundancies of video and figure out three key challenging issues in video coding; 2) Subsequently, we ...
Electrical engineering Optimization and learning based video coding UNIVERSITY OF CALIFORNIASAN DIEGO Truong Q. Nguyen AnCheolhongThe complexity of video coding standards has increased significantly from H.262/MPEG-2 to H.264/AVC in order to increase coding efficiency. Complexity mainly was increased ...
In this section, we start by briefly describing several existing works related to multimedia data computing in IoT for video coding. Then, we will present deep CNN-based in-loop filtering methods. 2.1 Video coding for M-IoT M-IoT poses several challenges to identify data transmission methods th...
However, deep learning-based video coding remains in its infancy. This paper reviews the representative works about using deep learning for image/video coding, which has been an actively developing research area since the year of 2015. We divide the related works into two categories: new coding ...
In this paper, we propose a machine learning-based fast coding unit (CU) depth decision method for High Efficiency Video Coding (HEVC), which optimizes the complexity allocation at CU level with given rate-distortion (RD) cost constraints. First, we analyze quad-tree CU depth decision process...
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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...
Video Target Tracking Based on Online Learning—深度学习在目标跟踪中的应用 摘要 近年来,深度学习方法在物体跟踪领域有不少成功应用,并逐渐在性能上超越传统方法。本文先对现有基于深度学习的目标跟踪算法进行了分类梳理,后续会分篇对各个算法进行详细描述。