Scalar and Vector Quantization for Learned Image Compression: A Study on the Effects of MSE and GAN Loss in Various Spaces

Löhdefink, Jonas; Hüger, Fabian; Schlicht, Peter; Fingscheidt, Tim

doi:10.1109/itsc45102.2020.9294350

Cited by 5 publications

(1 citation statement)

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“…Each corresponding pixel in fake and real images are compared, and the MSE score is calculated. The MSE metric provides better quality scores [29]. Converging and meaningful results for the synthetic wound generation task is obtained using the MSE metric.…”

Section: Data Collection Pre‐processing Environment and Validationmentioning

confidence: 99%

WG ² AN: Synthetic wound image generation using generative adversarial network

Sarp

Kuzlu

Wilson³

et al. 2021

The Journal of Engineering

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In part due to its ability to mimic any data distribution, Generative Adversarial Network (GAN) algorithms have been successfully applied to many applications, such as data augmentation, text-to-image translation, image-to-image translation, and image inpainting. Learning from data without crafting loss functions for each application provides broader applicability of the GAN algorithm. Medical image synthesis is also another field that the GAN algorithm has great potential to assist clinician training. This paper proposes a synthetic wound image generation model based on GAN architecture to increase the quality of clinical training. The proposed model is trained on chronic wound datasets with various sizes taken from real hospital environments. Hyperparameters such as epoch count and dataset size for training tasks are studied to find optimum training conditions as well. The performance of the developed model was evaluated through a mean squared error (MSE) metric to determine the similarity between generated and actual wounds. Visual inspection is performed to examine generated wound images. The results show that the proposed synthetic wound image generation (WG 2 AN) model has great potential to be used in medical training and performs well in producing synthetic wound images with a 1000-image training dataset and 200 epochs of training.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Data Collection Pre‐processing Environment and Validationmentioning

confidence: 99%