Most of the traditional video conferencing are standard SIP or H.323 architecture. There will be a single point of performance bottlenecks if large-scale video conference or a larger number of conferences emerge at the same time. In this paper, we proposed a new architecture of Multipoint Video Conference which can support a lot of meetings simultaneously using P2P and application multicast technology. In the side of server, the component called Media Control Server (MCS) is responsible for the media data process and transmission path selection. All the MCSs form the MCS overlay. The capacity and conference state information of User Equipments (UE) are stored in the MCS overlay. MCSs which be selected process all the transcoding and transmission task of audio and video for a meeting. In the side of user, some UEs which have enough bandwidth or other conditions could also undertake parts of media data process. MCS that service to the first UE in a conference decides which audio media process task could be assigned to Conference UEs; video media process task is allocated by Service MCS of video source. There are several methods to decide the transmission path, such as delay by server, application layer multicast or Hungarian method. At last, analysis is given to demonstrate the effectiveness of our architecture.
With the development of multimedia technology, more and more applications used in digital image processing, tampering with digital images is become easier. This paper proposes a new approach for image detection based on color tampering by encoding multiple color channels features based on VLAD without embedding watermarks. We have counted multiple sets of common color channels in computer vision, choosing the most suitable combination of color channels, and then using VLAD to encode these selected features, finally to train a SVM model by encoded features, we also found that when a lot of classes image were mixed together cannot be will detected, so we adopted deep learning to train a ResNet of classification, and first to classify our dataset, in comparison with state-of-theart method, various experiments result prove that the proposed approach achieves better performance in computer generated colorized image forgery detection.
The surface thermal environment plays an important role in urban sustainable development and ecological environment protection. Existing researches mainly focus on the formation process and mechanism of the surface thermal environment and lack the analysis of its effect on the lake ecological environment under the influence of human activities. Therefore, based on the analysis of the variations in land surface temperature (LST) and lake surface water temperature (LSWT) of Dianchi Lake at multiple spatio-temporal scales, this study evaluated the response of LSWT by using the methods of spatial influence, the center of gravity migration trajectory, trend analysis, and correlation analysis. The results show that: (1) Urbanization has a greater warming effect on LSWT than on LST, and the warming effect at night is greater than that at daytime. From 2001 to 2018, the warming trend of LSWT in daytime and night was 0.01°C/a and 0.02°C/a, respectively, while the cooling trend of LST in daytime was −0.03°C/a and the warming trend of LST in night was 0.01°C/a. (2) Areas with high human activity are warming faster, both in the eastern and northern coastal areas of lake and the heavily urbanized sub-basins. (3) The spatial influence of LST and LSWT are highly correlated, and the response of the outer buffer in the range of 2 km is obvious, and the direction of gravity center migration trajectory is consistent. The results are of great significance for the control and improvement of urban heat island and ecological environment protection of Dianchi Lake in Kunming and can provide data support and decision support for urban planning, promoting the construction of the ecological civilization city in Kunming, and reducing the accumulation of urban surface heat.
Abstract Lake surface water temperature (LSWT) is an important factor of water ecological environment. In the context of global warming, the LSWT of global lakes generally reveals an upward trend. With a continuous intensification of human activities and a rapid expansion of the impervious surface, urbanization has exerted an increasing impact on the environment, so the impact of human activities on LSWT cannot be ignored. Because of the special geographical location, the change of LSWT in plateau lakes has important impacts on climate diversity, biodiversity, and cultural diversity. As a result, it is critical to monitor and model the variation characteristics of LSWT in the plateau area. Based on the data set of natural factors representing climate change and human factors representing human activities, this study proposes a classification of lake types by K‐Means clustering method. At watershed scale, 11 lakes in the study area are divided into three types: Natural Lake, Semi‐urban Lake, and Urban Lake (UL). Based on this classification, the variation characteristics of LSWT for the eleven lakes from 2001 to 2017 are analyzed. The causal relationship and contribution of climate change and human activities to the rise of LSWT are discussed. Results show that (1) from 2001 to 2017, the annual mean of LSWT‐day/night and near‐surface air temperature in the 11 lakes show a warming trend, a significant correlation ( R = 0.82, α = 0.0164 < 0.5) and a same periodicity, which indicates that near‐surface air temperature is one of the main influencing factors of LSWT warming in Yunnan‐Guizhou Plateau. (2) LSWT warming trend of UL is more obvious than those of Semi‐urban Lake and Natural Lake, indicating that human activities have more significant impact on LSWT of UL. The main driving factors are the impervious surface expansion and population increase. (3) The influence of human activities on the LSWT in Yunnan‐Guizhou Plateau is becoming more and more significant, and it is also the main factor in causing the deterioration of lake water environment in Yunnan‐Guizhou Plateau.
An algorithm is proposed to detect abrupt and gradual scene changes in H.264 compressed videos. In H.264, the inter prediction in the encoding process generates the DC component of the prediction residual. In this paper, we use the DC component of inter prediction residuals as well as motion vectors to detect scene changes. Using the proposed algorithm, we can detect scene changes for H.264 compressed videos more effectively, as demonstrated by simulations.
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