Perceptual Learned Image Compression via End-to-End JND-Based
  Optimization
Farhad Pakdaman Sanaz Nami Moncef Gabbouj
Emerging Learned image Compression (LC) achieves significant improvements in
coding efficiency by end-to-end training of neural networks for compression. An
important benefit of this approach over traditional codecs is that any
optimization criteria can be directly applied to the encoder-decoder networks
during training. Perceptual optimization of LC to comply with the Human Visual
System (HVS) is among such criteria, which has not been fully explored yet.
This paper addresses this gap by proposing a novel framework to integrate Just
Noticeable Distortion (JND) principles into LC. Leveraging existing JND
datasets, three perceptual optimization methods are proposed to integrate JND
into the LC training process: (1) Pixel-Wise JND Loss (PWL) prioritizes
pixel-by-pixel fidelity in reproducing JND characteristics, (2) Image-Wise JND
Loss (IWL) emphasizes on overall imperceptible degradation levels, and (3)
Feature-Wise JND Loss (FWL) aligns the reconstructed image features with
perceptually significant features. Experimental evaluations demonstrate the
effectiveness of JND integration, highlighting improvements in rate-distortion
performance and visual quality, compared to baseline methods. The proposed
methods add no extra complexity after training.