IEEE Signal Processing Society Santa Clara Valley Chapter

Description The past decades have witnessed rapid growth in imaging as a major form of communication between individuals. Due to recent advances in capture, storage, delivery, and display technologies, consumers demand improved perceptual quality while requiring reduced storage. In this context, research and innovation in lossy image compression have steered towards methods capable of achieving high compression ratios without compromising the perceived visual quality of images.  Subjective and objective visual quality assessment of images plays a fundamental role in defining quality as perceived by human observers. Although the field of image compression is constantly evolving towards efficient solutions for higher visual qualities, standardized subjective visual quality assessment protocols are still limited to those proposed in ITU-R Recommendation BT.500 and JPEG AIC standards. Similarly, objective image quality metrics often exhibit limited correlation with subjective quality scores across various distortion types and intensities, and little to no work has been devoted to assessing the performance of these metrics on images with compression artifacts in the high to nearly visually lossless quality range.  This talk discusses the effectiveness of both subjective and objective image quality assessment methods on coding artifacts with higher visual quality. Moreover, ongoing solutions investigated in the context of the JPEG AIC activity are presented. Speaker Bio Michela Testolina obtained her B.Sc. and M.Sc. in Telecommunication Engineering from the University of Trento, Italy. Since 2020 she has been a Ph.D. student at the Multimedia Signal Processing Group (MMSPG) under the supervision of Prof. Touradj Ebrahimi. Her main research interests are in the field of multimedia compression and quality assessment, both subjective and objective. She actively contributes to the standardization activities, namely to the Joint Photographic Experts Group (JPEG) where she is chairing the Ad Hoc Group on Image Quality Assessment (JPEG AIC) and contributes to the JPEG AI and JPEG DNA activities.