Channel equalizer performance analysis for Hybrid 3DTV in ATSC 3.0 standard
Recently, next generation broadcasting standardization has being conducted to transmit UHDTV contents. In this paper, an equalizer using least square estimation is designed and reception performance is analyzed for Hybrid 3DTV transceiver system. Equalizer structure for Hybrid 3DTV is designed by referring to ATSC 3.0 PHY layer. Hybrid 3DTV uses time division multiplexing for transmitting two physical layer pipes. The equalizer performance is simulated on Pedestrian Indoor channel which has long echoes. Simulation result shows that equalizer performance for Hybrid 3DTV is improved about 5 dB.
Bandwidth-efficient stereoscopic 3D broadcasting system utilizing mobile broadcast networks
In this paper, we propose a bandwidth-efficient stereoscopic 3D broadcasting system named Service Compatible 3D-TV using Main and Mobile Hybrid Delivery (SC-MMH), which incor-porates fixed and mobile broadcasting services to provide 3D service without allocating additional bandwidth. By combining two existing broadcast networks, the proposed system provides high quality 3D services, fully guarantees backward-compatibility with the legacy fixed/mobile 2D receivers, and maximizes the channel efficiency. System structure of the proposed system and the objective evaluation that we conducted to find the optimum bitrate distribution are presented in this paper.
Integrating Local CNN and Global CNN for Script Identification in Natural Scene Images
Script identification in natural scene images is a key pre-step for text recognition and is also an indispensable condition for automatic text understanding systems that are designed for multi-language environments. In this paper, we present a novel framework integrating Local CNN and Global CNN both of which are based on ResNet-20 for script identification. We first obtain a lot of patches and segmented images based on the aspect ratios of the images. Subsequently, these patches and segmented images are used as inputs to Local CNN and Global CNN for training, respectively. Finally, to get the final results, the Adaboost algorithm is used to combine the results of Local CNN and Global CNN for decision-level fusion. Benefiting from such a strategy, Local CNN fully exploits the local features of the image, effectively revealing subtle differences among the scripts that are difficult to distinguish such as English, Greek, and Russian. Moreover, Global CNN mines the global features of the image to improve the accuracy of script identification. The experimental results demonstrate that our approach has a good performance on four public datasets.