Recently, the advancements in the field of wireless technologies and micro-electro-mechanical systems lead to the development of potential applications in wireless sensor networks (WSNs). The visual sensors in WSN create a significant impact on computer vision based applications such as pattern recognition and image restoration. generate a massive quantity of multimedia data. Since transmission of images consumes more computational resources, various image compression techniques have been proposed. But, most of the existing image compression techniques are not applicable for sensor nodes due to its limitations on energy, bandwidth, memory, and processing capabilities. In this article, we introduce a highly reliable and low-complexity image compression scheme using neighborhood correlation sequence (NCS) algorithm. The NCS algorithm performs the bit reduction operation and then encoded by a codec (such as PPM, Deflate, and Lempel Ziv Markov chain algorithm.) to further compress the image. The proposed NCS algorithm increases the compression performance and decreases the energy utilization of the sensor nodes with high fidelity. Moreover, it achieved a minimum end to end delay of 1074.46 ms at the average bit rate of 4.40 bpp and peak signal to noise ratio of 48.06 on the applied test images. On comparing with state-of-art methods, the proposed method maintains a better tradeoff between compression efficiency and reconstructed image quality.

Highly Reliable and Low-Complexity Image Compression Scheme Using Neighborhood Correlation Sequence Algorithm in WSN

With the advent of the Internet age, network information technology is rapidly entering college English classes, which fundamentally changes the mode of college English teaching. In college English...

College Business English teaching in the context of multimedia network

Feature extraction plays a significant part in computer vision tasks. In this paper, we propose a method which transfers rich deep features from a pretrained model on face verification task and feeds the features into Bayesian ridge regression algorithm for facial beauty prediction. We leverage the deep neural networks that extracts more abstract features from stacked layers. Through simple but effective feature fusion strategy, our method achieves improved or comparable performance on SCUT-FBP dataset and ECCV HotOrNot dataset. Our experiments demonstrate the effectiveness of the proposed method and clarify the inner interpretability of facial beauty perception.

Transferring Rich Deep Features for Facial Beauty Prediction.

Consumer behaviour analysis for business development

Remote video collaboration is common nowadays in conferencing, telehealth and remote teaching applications. To support these low-latency and interactive use cases, Real-Time Communication (RTC) solutions are generally used. WebRTC is an open-source project for real-time browser-based conferencing, developed with a peer-to-peer architecture in mind. In this peer-to-peer architecture, each sending peer needs to encode a separate, independent stream for each receiving peer participating in the remote session, which makes this approach expensive in terms of encoders and not able to scale well for a large number of users. This paper proposes a WebRTC-compliant framework to solve this scalability issue, without impacting the quality delivered to the remote peers. In the proposed framework, each sending peer is only equipped with a limited number of encoders, much smaller than and independent of the number of receiving peers. Consequently, each encoder transmits to a multitude of receivers at the same time, to improve scalability. A centralized node based on the Selective Forwarding Unit (SFU) principle, called conference controller, forwards the best stream to the receiving peers, based on their bandwidth conditions. Moreover, the conference controller dynamically recomputes the encoding bitrates of the sending peers, to maximize the quality delivered to the receiving peers. This approach allows to closely follow the long-term bandwidth variations of the receivers, even with a limited number of encoders at sender-side, and increase the delivered video quality. An integer linear programming formulation for the bitrate recomputation problem is presented, which can be optimally solved when the number of receivers is small. An approximate, scalable method is also proposed using the K-means clustering algorithm. The gains brought by the proposed framework have been confirmed in both simulation and emulation, through a testbed implementation using the Google Chrome browser and the open-source Jitsi-Videobridge software. Particularly, we focus on a remote collaboration scenario where the interaction among the remote participants is dominated by a single peer, as in a remote teaching scenario. When a single sending peer equipped with three encoders transmits to 28 receiving peers, the proposed framework improves the average received video bitrate up to 15%, compared to a static solution where the encoding bitrates do not change over time. Moreover, the dynamic bitrate recomputation is more efficient than a static association in terms of encoders used at sender-side. For the same configuration mentioned above, the same received bitrate is obtained in the static case using four encoders as in the dynamic case using three encoders.

A scalable WebRTC-based framework for remote video collaboration applications

Automatic removal of complex shadows from indoor videos using transfer learning and dynamic thresholding

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Automatic removal of complex shadows from indoor videos using transfer learning and dynamic thresholding