Assessing objective quality metrics for JPEG and MPEG point cloud coding
arxiv(2024)
摘要
As applications using immersive media gain increasing attention from both
academia and industry, research in the field of point cloud compression has
greatly intensified in recent years, leading to the development of the MPEG
compression standards V-PCC and G-PCC, as well as the more recent JPEG Pleno
learning-based point cloud coding. Each of the above-mentioned standards is
based on a different algorithm, introducing distinct types of degradation that
may impair the quality of experience when high lossy compression is applied.
Although the impact on perceptual quality could be accurately evaluated during
subjective quality assessment experiments, objective quality metrics serve as
predictors of the visually perceived quality and provide similarity scores
without human intervention. Nevertheless, their accuracy can be susceptible to
the characteristics of the evaluated media as well as to the type and intensity
of the added distortion. While the performance of multiple state-of-the-art
objective quality metrics has already been evaluated through their correlation
with subjective scores obtained in the presence of artifacts produced by the
MPEG standards, no study has evaluated how metrics perform with the more recent
JPEG Pleno point cloud coding. In this paper, a study is conducted to benchmark
the performance of a large set of objective quality metrics in a subjective
dataset including distortions produced by JPEG and MPEG codecs. The dataset
also contains three different trade-offs between color and geometry compression
for each codec, adding another dimension to the analysis. Performance indexes
are computed over the entire dataset but also after splitting according to the
codec and to the original model, resulting in detailed insights about the
overall performance of each visual quality predictor as well as their
cross-content and cross-codec generalization ability.
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