Semantic Aware Just Noticeable Differences for VVC Compressed Text Screen Content Images

With the rapid development of multimedia applications such as online education, remote conferences, and telemedicine, an emerging type of image known as the text screen content image (TSCI) has gained widespread utilization. Distinguishing from natural images captured by cameras, TSCI is generally generated or rendered by computers and exhibits significant differences in content characteristics. Notably, TSCI primarily comprises text, which is a symbol system uniquely defined by humans with specific semantics. As an important carrier for transmitting semantic information, the quality of text in TSCI significantly affects the subjective perception experience of multimedia system users. Just noticeable difference (JND) is a widely studied image quality measure that is theoretically closest to human perception. However, the traditional JND (T-JND) experiments fail to distinguish text from other image contents, ignoring the significant impact of text semantic readability on image quality. This paper, for the first time, focuses on the impact of text semantics on the quality of TSCI, and JND experiments for TSCIs compressed by the state-of-the-art versatile video coding (VVC) standard are explored and discussed. Specifically, a matching TSCI dataset is first established. Using the dataset, image subjective observation experiments are further designed and carried out to construct the traditional JND (T-JND) experiment as well as the semantic aware JND (S-JND) experiment. By comparing the experimental results, crucial conclusions are reached, including the fact that the S-JND experiment provides a more precise description of the TSCI quality compared to the T-JND experiment. These conclusions have important guiding significance for the subsequent development of efficient JND models suitable for TSCIs compressed by VVC.