• Journal of Broadcast Engineering
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• v.18 no.6
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• pp.895-910
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• 2013

Multi-screen service is a service that consumes more than one media in a number of terminals simultaneously or discriminately. This multi-screen service has become useful due to distribute of smart TV and terminals. Also, in case of hybrid broadcasting environment that is convergence of broadcasting and communication environment, it is able to provide various user experience through contents consumed by multiple screens. In hybrid broadcasting environment, scene composition technology can be used as an element technology for multi-screen service. Using scene composition technology, multiple media can be consumed complexly through the specified presentation time and space. Thus, multi-screen service based on the scene composition technology can provide spatial and temporal control and consumption of multiple media by linkage between the terminals. However, existing scene composition technologies are not able to use easily in hybrid broadcasting because of applicable environmental constraints, the difficulty in applying the various terminal and complexity. For this problems, HTML5 can be considered. HTML5 is expected to be applied in various smart terminals commonly, and provides consumption of diverse media. So, in this paper, it proposes the scene composition and multi-screen service technology based on HTML5 that is expected be used in various smart terminals providing hybrid broadcasting environment. For this, it includes the introduction in terms of HTML5 and multi-screen service, the method of providing information related with scene composition and multi-screen service through the extention of elements and attributes in HTML5, media signaling between terminals and the method of synchronization. In addition, the proposed scene composition and multi-screen service technology based on HTML5 was verified through the implementation and experiment.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.453-462
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• 2021

A lot of license plate data is required for car number recognition. License plate data needs to be balanced from past license plates to the latest license plates. However, it is difficult to obtain data from the actual past license plate to the latest ones. In order to solve this problem, a license plate recognition study through deep learning is being conducted by creating a synthetic license plates. Since the synthetic data have differences from real data, and various data augmentation techniques are used to solve these problems. Existing data augmentation simply used methods such as brightness, rotation, affine transformation, blur, and noise. In this paper, we apply a style transformation method that transforms synthetic data into real-world data styles with data augmentation methods. In addition, real license plate data are noisy when it is captured from a distance and under the dark environment. If we simply recognize characters with input data, chances of misrecognition are high. To improve character recognition, in this paper, we applied the DeblurGANv2 method as a quality improvement method for character recognition, increasing the accuracy of license plate recognition. The method of deep learning for license plate detection and license plate number recognition used YOLO-V5. To determine the performance of the synthetic license plate data, we construct a test set by collecting our own secured license plates. License plate detection without style conversion recorded 0.614 mAP. As a result of applying the style transformation, we confirm that the license plate detection performance was improved by recording 0.679mAP. In addition, the successul detection rate without image enhancement was 0.872, and the detection rate was 0.915 after image enhancement, confirming that the performance improved.