Mr. Takuya Suzuki Profile

Takuya Suzuki

at Sharp Corp

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Proceedings Article | 26 March 2023 Paper

A prior-guided face image super-resolution network based on attention mechanism

Yun Liu, Remina Yano, Hiroshi Watanabe, Takuya Suzuki, Zheming Fan, Takeshi Chujoh, Tomohiro Ikai

Proceedings Volume 12592, 1259213 (2023) https://doi.org/10.1117/12.2666732

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In this paper, we propose a face image super-resolution method, aiming to reconstruct high quality face images from a low resolution input. The proposed method introduces an attentional multi-scale feature fusion block, which aims to improve the representation power of the neural network by emphasizing the important feature maps and suppressing the unimportant ones. In addition, the facial prior information is utilized by adding a separate prior branch, an hourglass structure is used. Experiments show that the face images reconstructed by the proposed method exhibit noticeable quality improvement compared to the low-resolution images and other SR approaches.

Proceedings Article | 13 March 2021 Paper

Multi-frame super-resolution for versatile video coding

Toshiya Hori, Remina Yano, Hiroshi Watanabe, Takeshi Chujoh, Tomohiro Ikai, Eiichi Sasaki, Takuya Suzuki, Norio Ito

Proceedings Volume 11766, 1176604 (2021) https://doi.org/10.1117/12.2590958

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The latest video coding standard, Versatile Video Coding (VVC), has been finalized in July 2020. In traditional codecs, it is not possible to refer to different resolution pictures in inter-picture prediction except for inter layer cases. However, VVC does support Reference Picture Resampling (RPR), which allows pictures to refer to different resolution pictures so that video resolution can be adaptively switched in sequence. This work’s objective is to improve compression efficiency in the case of RPR. In the preliminary experiments, the compression efficiencies of reduced size encoded case where frames are encoded in half size and up-scaled to their original size and original size encoded case are compared. The result shows that most sequences get worse BD-rate when the sequences are encoded/decoded in reduced size. Here, it is noted that up-scaling is done by VVC Test Model, VTM-10.0, which uses a linear interpolation filter. Thus, the frames cannot recover high-frequency components sufficiently and cannot use temporal correlation. Therefore, this paper proposes a multi-frame super-resolution between frames of different resolutions where some frames are encoded in high quality frames in their original size and other frames in half size by using RPR. The proposed method uses the original size frames, which contain a lot of information, to perform super-resolution to the reduced frame. By doing so, it is observed it saves the bitrate by reducing the frame resolution and increases the quality of reduced frames by using multiframe super-resolution, which leads to improving the compression efficiency.

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