IQNet: Image Quality Assessment Guided Just Noticeable Difference
  Prefiltering For Versatile Video Coding
Yu-Han Sun Chiang Lo-Hsuan Lee Tian-Sheuan Chang
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.