Quality Assessment of Light Field Images

Overview & Synopsis

Light Field Image (LFI) has garnered remarkable interest and fascination due to its burgeoning significance in immersive applications. Since LFIs may be distorted at various stages from acquisition to visualization, Light Field Image Quality Assessment (LFIQA) is vitally important to monitor the potential impairments of LFI quality. The first contribution (Chapter 3) of this work focuses on developing two handcrafted feature-based No-Reference (NR) LFIQA metrics, in which texture information and wavelet information are exploited for quality evaluation. Then in the second part (Chapter 4), we explore the potential of combining deep learning technology with the quality assessment of LFIs, and propose four deep learning-based LFIQA metrics according to different LFI characteristics, including three NR metrics and one Full-Reference (FR) metric. In the last part (Chapter 5), we conduct subjective experiments and propose a novel standard LFIQA database. Moreover, a benchmark of numerous state-of-the-art objective LFIQA metrics on the proposed database is provided.