Current Issue : October-December Volume : 2021 Issue Number : 4 Articles : 5 Articles
Future Video Coding (FVC) is a modern standard in the field of video coding that offers much higher compression efficiency than the HEVC standard. FVC was developed by the Joint Video Exploration Team (JVET), formed through collaboration between the ISO/IEC MPEG and ITU-T VCEG. New tools emerging with the FVC bring in super resolution implementation schemes that are being recommended for Ultra-High-Definition (UHD) video coding in both SDR and HDR images. However, a new flexible block structure is adopted in the FVC standard, which is named quadtree plus binary tree (QTBT) in order to enhance compression efficiency. In this paper, we provide a fast FVC algorithm to achieve better performance and to reduce encoding complexity. First, we evaluate the FVC profiles under All Intra, Low-Delay P, and Random Access to determine which coding components consume the most time. Second, a fast FVC mode decision is proposed to reduce encoding computational complexity. Then, a comparison between three configurations, namely, Random Access, Low-Delay B, and Low-Delay P, is proposed, in terms of Bitrate, PSNR, and encoding time. Compared to previous works, the experimental results prove that the time saving reaches 13% with a decrease in the Bitrate of about 0.6% and in the PSNR of 0.01 to 0.2 dB....
In recent years, the use of drones for surveillance tasks has been on the rise worldwide. However, in the context of anomaly detection, only normal events are available for the learning process. Therefore, the implementation of a generative learning method in an unsupervised mode to solve this problem becomes fundamental. In this context, we propose a new end-to-end architecture capable of generating optical flow images from original UAV images and extracting compact spatiotemporal characteristics for anomaly detection purposes. It is designed with a custom loss function as a sum of three terms, the reconstruction loss (Rl ), the generation loss (Gl ) and the compactness loss (Cl ) to ensure an efficient classification of the “deep-one” class. In addition, we propose to minimize the effect of UAV motion in video processing by applying background subtraction on optical flow images. We tested our method on very complex datasets called the mini-drone video dataset, and obtained results surpassing existing techniques’ performances with an AUC of 85.3....
This study examines the factors influencing early paid Over-The-Top (OTT) video streaming market growth in 50 countries. The results of the panel data analysis suggest that Netflix’s market entry, traditional pay TV market size, broadband infrastructure, and OTT platform competition contribute to the early market growth of paid OTT video streaming services, such as subscription video-on-demand (SVOD) services. The results also reveal that the traditional pay TV subscription market and the paid OTT video streaming market initially grow together in many countries. However, the findings also reveal a negative association between the market entry of Netflix and the subscription revenue growth rate of traditional pay TV services. The results of this study suggest industry and policy implications for paid OTT video streaming market growth and the sustainable development of the media industry....
The exponential growth of user-generated content has increased the need for efficient video summarization schemes. However, most approaches underestimate the power of aural features, while they are designed to work mainly on commercial/professional videos. In this work, we present an approach that uses both aural and visual features in order to create video summaries from user-generated videos. Our approach produces dynamic video summaries, that is, comprising the most “important” parts of the original video, which are arranged so as to preserve their temporal order. We use supervised knowledge from both the aforementioned modalities and train a binary classifier, which learns to recognize the important parts of videos. Moreover, we present a novel user-generated dataset which contains videos from several categories. Every 1 sec part of each video from our dataset has been annotated by more than three annotators as being important or not. We evaluate our approach using several classification strategies based on audio, video and fused features. Our experimental results illustrate the potential of our approach....
Integrating network technology fully into traditional teaching can realize resource sharing to the greatest extent, so the establishment of distance education and network teaching platform has become the inevitable development of the time. *e purpose of this paper is to build an embedded mobile teaching model based on network streaming media technology. *e technology application, system composition and structure, realization process, and teaching method of the system are introduced in detail. *e system energy consumption, bit rate of video information, and buffer technology were optimized, respectively. In this system, the energy consumption optimization method of mobile streaming media is adopted, and Central Processing Unit (CPU) resources are allocated reasonably according to the principle of maximizing rewards, so as to achieve the purpose of reducing power consumption. *e results show that the system can effectively ensure the normal transmission of large multimedia information stream data through the network by using streaming media playback technology, and users can control the teaching process through interactive operation, which makes the network multimedia distance teaching based on streaming media develop in an all-round way and bring advanced teaching mode for education....
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