Redefining Visual Quality: The Impact of Loss Functions on INR-Based Image Compression

Implicit Neural Representations (INR) are a novel data representation technique which is gaining ground in the image compression field due to its simplicity and interesting results in terms of rate/distortion ratio. Although a variety of methods based on this paradigm were proposed, limited interest has been given to the analysis of the loss function and the impact of compression artifacts on the visual quality of the reconstructed images, which are mainly due to the adoption of the simple Mean Squared Error (MSE) loss function and to the evaluation done merely in terms of Peak Signal-to-Noise Ratio (PSNR), which do not often correlate with the human perception. In this paper, we evaluate a set of five loss functions in the context of training INRs for image compression, applied to three state-of-the-art architectures, and evaluate their effect on a broader collection of quantitative metrics and the visual fidelity of the decoded images to the originals. The presented outcomes show that the reconstructions obtained by training with some loss functions as MSE suffer from over-smoothing and aliasing artifacts. Our findings reveal that through the employing of a suitable loss function, state-of-the-art architectures quantitatively and qualitatively outperform the results reported in their original papers.