TY - GEN
T1 - Applying Holo360 Video and Image Super-Resolution Generative Adversarial Networks to Virtual Reality Immersion
AU - Feng, Chia Hui
AU - Hung, Yu Hsiu
AU - Yang, Chao Kuang
AU - Chen, Liang Chi
AU - Hsu, Wen Cheng
AU - Lin, Shih Hao
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Super-resolution deep learning methods focus on image processing solutions and discussions in two-dimensional super-resolution image processing NOT for 360 equirectangular images. Therefore, the motivation of this research is to establish the deep learning network model Holo360 SRGAN and data set of 360 equirectangular images, and observe whether the sharpness and noise of Holo360 SRGAN compared with the original image reach the optical verification standard. The results of this study point out two significant points: 1) For a convolution training core neuron with the best model architecture of Holo360 SRGAN with 360 images 8 K (8192 × 4096 px), FOV: 360°, the expanded the convolution core neuron size as 5 × 5 to contains more learning features. And 2) Holo360 SRGAN image experiment results, 6 ROI optical analysis clarity increased by 27%, and sharpness increased by 42%. The experimental original image noise SNR is 28.2 dB, and the Holo360 SRGAN (×2) noise SNR is 36.8 dB, so it is increased by +8.6 dB, and the amount of image detail is also increased. Contributions enhance the super-resolution visual experience of equirectangular video or image.
AB - Super-resolution deep learning methods focus on image processing solutions and discussions in two-dimensional super-resolution image processing NOT for 360 equirectangular images. Therefore, the motivation of this research is to establish the deep learning network model Holo360 SRGAN and data set of 360 equirectangular images, and observe whether the sharpness and noise of Holo360 SRGAN compared with the original image reach the optical verification standard. The results of this study point out two significant points: 1) For a convolution training core neuron with the best model architecture of Holo360 SRGAN with 360 images 8 K (8192 × 4096 px), FOV: 360°, the expanded the convolution core neuron size as 5 × 5 to contains more learning features. And 2) Holo360 SRGAN image experiment results, 6 ROI optical analysis clarity increased by 27%, and sharpness increased by 42%. The experimental original image noise SNR is 28.2 dB, and the Holo360 SRGAN (×2) noise SNR is 36.8 dB, so it is increased by +8.6 dB, and the amount of image detail is also increased. Contributions enhance the super-resolution visual experience of equirectangular video or image.
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U2 - 10.1007/978-3-030-49059-1_42
DO - 10.1007/978-3-030-49059-1_42
M3 - Conference contribution
AN - SCOPUS:85088752451
SN - 9783030490584
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 569
EP - 584
BT - Human-Computer Interaction. Design and User Experience - Thematic Area, HCI 2020, Held as Part of the 22nd International Conference, HCII 2020, Proceedings
A2 - Kurosu, Masaaki
PB - Springer
T2 - Thematic Area on Human Computer Interaction, HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
Y2 - 19 July 2020 through 24 July 2020
ER -