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Evaluating retargeting image operators is a subjective task and, therefore, challenging to execute without human interference. Image Retargeting Quality Algorithms execute this task, giving some score to the retargeted image and, usually, trying to get a result similar to a human opinion since humans generally agree with each other on the quality of a resized image. Therefore, we propose an Autoencoder-based IRQA named AutoEncoder Information MaP Similarity (AEIMPS) to address this task using the NVAE architecture. In our experiments, besides the retargeting ratio, we use the latent space and the reconstructed image in the IRQA. AIEMPS achieved an average performance compared to other IRQAs in the literature. |
