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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.