Generative Adversarial Network and ResNet Comparison for Video Super Resolution in Smartphones

  • André Barbosa da Vitória IFES
  • Francisco de Asis Boldt IFES
  • Hilario Seibel Junior IFES

Resumo


With the latest evolutions of smartphone hardware regarding neural network processing and advances in Image Super Resolution, one question remains: What is the applicability of SR techniques already consolidated in smartphones? Several works focused on different characteristics of images have been developed and seek to understand how such networks can meet different segments. This work focuses on applying two already consolidated networks in the processing of photo-realistic images, SRResNet and SRGAN, in mobile devices to identify how such networks behave in smartphones of different generations, either in the image quality or in the response time of these networks. After evaluation, the SRResNet network had a better performance both in inferred image quality and lower latency, with a PSNR of 27.7075 versus 21.3843 and 0.19 milliseconds latency, compared to 0.20 milliseconds for SRGAN, thus showing that it is feasible to apply SR techniques already consolidated in smartphones.

Palavras-chave: super resolution, smartphones, neural network, deep learning

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Publicado
13/11/2023
VITÓRIA, André Barbosa da; BOLDT, Francisco de Asis; SEIBEL JUNIOR, Hilario. Generative Adversarial Network and ResNet Comparison for Video Super Resolution in Smartphones. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 102-107. DOI: https://doi.org/10.5753/wvc.2023.27540.