How to Evaluate Semantic Communications for Images with ViTScore Metric?
Tingting Zhu Bo Peng Jifan Liang Tingchen Han Hai Wan Jingqiao Fu Junjie Chen
Semantic communications (SC) have been expected to be a new paradigm shifting
to catalyze the next generation communication, whose main concerns shift from
accurate bit transmission to effective semantic information exchange in
communications. However, the previous and widely-used metrics for images are
not applicable to evaluate the image semantic similarity in SC. Classical
metrics to measure the similarity between two images usually rely on the pixel
level or the structural level, such as the PSNR and the MS-SSIM.
Straightforwardly using some tailored metrics based on deep-learning methods in
CV community, such as the LPIPS, is infeasible for SC. To tackle this, inspired
by BERTScore in NLP community, we propose a novel metric for evaluating image
semantic similarity, named Vision Transformer Score (ViTScore). We prove
theoretically that ViTScore has 3 important properties, including symmetry,
boundedness, and normalization, which make ViTScore convenient and intuitive
for image measurement. To evaluate the performance of ViTScore, we compare
ViTScore with 3 typical metrics (PSNR, MS-SSIM, and LPIPS) through 5 classes of
experiments. Experimental results demonstrate that ViTScore can better evaluate
the image semantic similarity than the other 3 typical metrics, which indicates
that ViTScore is an effective performance metric when deployed in SC scenarios.