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Temporally adaptive and region-selective signaling of applying multiple neural network models
Sehwan Ki(기세환),Munchurl Kim(김문철) 한국방송·미디어공학회 2020 한국방송공학회 학술발표대회 논문집 Vol.2020 No.11
The fine-tuned neural network (NN) model for a whole temporal portion in a video does not always yield the best quality (e.g., PSNR) performance over all regions of each frame in the temporal period. For certain regions (usually homogeneous regions) in a frame for super-resolution (SR), even a simple bicubic interpolation method may yield better PSNR performance than the fine-tuned NN model. When there are multiple NN models available at the recievers where each NN model is trained for a group of images having a specific category of image characteristics, the performance of quality enhancement can be improved by seletively applying an appropriate NN model for each image region according to its image characteristic category to which the NN model was dedicatedly trained. In this case, it is necessary to signal which NN model is applied for each region. This is very advantageous for image restoration and quality enhancement (IRQE) application at user terminals with limited computing capabilities.
초협대역 비디오 전송을 위한 심층 신경망 기반 초해상화를 이용한 스케일러블 비디오 코딩
김대은(Dae-Eun Kim),기세환(Sehwan Ki),김문철(Munchurl Kim),전기남(Ki Nam Jun),백승호(Seung Ho Baek),김동현(Dong Hyun Kim),최증원(Jeung Won Choi) 한국방송·미디어공학회 2019 방송공학회논문지 Vol.24 No.1
The necessity of transmitting video data over a narrow-bandwidth exists steadily despite that video service over broadband is common. In this paper, we propose a scalable video coding framework for low-resolution video transmission over a very narrow-bandwidth network by super-resolution of decoded frames of a base layer using a convolutional neural network based super resolution technique to improve the coding efficiency by using it as a prediction for the enhancement layer. In contrast to the conventional scalable high efficiency video coding (SHVC) standard, in which upscaling is performed with a fixed filter, we propose a scalable video coding framework that replaces the existing fixed up-scaling filter by using the trained convolutional neural network for super-resolution. For this, we proposed a neural network structure with skip connection and residual learning technique and trained it according to the application scenario of the video coding framework. For the application scenario where a video whose resolution is 352×288 and frame rate is 8fps is encoded at 110kbps, the quality of the proposed scalable video coding framework is higher than that of the SHVC framework.