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Nowadays, deep-learning image coding solutions have shown similar or bettercompression efficiency than conventional solutions based on hand-craftedtransforms and spatial prediction techniques. These deep-learning codecsrequire a large training set of images and a training methodology to obtain asuitable model (set of parameters) for efficient compression. The training isperformed with an optimization algorithm which provides a way to minimize theloss function. Therefore, the loss function plays a key role in the overallperformance and includes a differentiable quality metric that attempts to mimichuman perception. The main objective of this paper is to study the perceptualimpact of several image quality metrics that can be used in the loss functionof the training process, through a crowdsourcing subjective image qualityassessment study. From this study, it is possible to conclude that the choiceof the quality metric is critical for the perceptual performance of thedeep-learning codec and that can vary depending on the image content.

Perceptual impact of the loss function on deep-learning image coding performance