Implicit Neural Representations for Image Compression

The main focuse of the current thesis proposal is related to performing image compression via Implicit Neural Representations based deep neural network architectures, specifically employing SIREN architectures, adopting pruning and quantization techniques for constraining in the number of weights parameters and their numerical representation, respectively, while measuring and evaluating induced performance image quality metrics to be related to achieved image compression bit rate calculated from deep neural network overall footprint. The results obtained while running and collecting data related to the involved image quality metrices, such as Pnsr score and bit rate, have suggested us as well as provided us evidence of how critical and still heavy task represents the attempt of reaching image compression throughout common Neural Network Compression Techniques applied directly to Neural Network Models. Finally, due to the vast number of potential suitable hyper-parameter configurations, we have noticed that there are chance to reduce the gap we can measure, in terms of performance, between well-established image compression methods such as Jpeg and SIREN compressed models.