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.

本论文通过将 JND 原则融入学习图像压缩（LC）的训练过程中，提出了三种感知优化方法以及评估结果，证明了 JND 整合对于增强速率 - 失真表现和视觉质量的有效性，相比基线方法，所提出的方法在训练后不增加额外复杂性。

通过端到端的 JND 优化实现的感知学习图像压缩

Perceptual Learned Image Compression via End-to-End JND-Based  Optimization

Image prefiltering with just noticeable distortion (JND) improves coding
efficiency in a visual lossless way by filtering the perceptually redundant
information prior to compression. However, real JND cannot be well modeled with
inaccurate masking equations in traditional approaches or image-level subject
tests in deep learning approaches. Thus, this paper proposes a fine-grained JND
prefiltering dataset guided by image quality assessment for accurate
block-level JND modeling. The dataset is constructed from decoded images to
include coding effects and is also perceptually enhanced with block overlap and
edge preservation. Furthermore, based on this dataset, we propose a lightweight
JND prefiltering network, IQNet, which can be applied directly to different
quantization cases with the same model and only needs 3K parameters. The
experimental results show that the proposed approach to Versatile Video Coding
could yield maximum/average bitrate savings of 41\%/15\% and 53\%/19\% for
all-intra and low-delay P configurations, respectively, with negligible
subjective quality loss. Our method demonstrates higher perceptual quality and
a model size that is an order of magnitude smaller than previous deep learning
methods.

提出了一种基于图像质量评估的细粒度就知道失真（JND）预过滤数据集和轻量级 JND 预过滤网络，用于准确建模分块级 JND，旨在提高可视无损压缩效率和感知质量。