Light Field Image (LFI) records both angular and spatial information and provides immersive experiences for observers by rendering a scene from multiple perspectives. To cope with the resolution limitations of capture hardware, LFI angular reconstruction and spatial super-resolution are two widely-used methods, but they can also induce some special types of distortions, especially when two methods are adopted in combination. To this end, new challenges have been brought in assessing the quality of these distorted LFIs. In this paper, firstly, we conduct subjective experiments to evaluate the distorted LFI quality and present a novel perceptual quality assessment database with the associated subjective quality scores. Specifically, the proposed database focuses on the distortions introduced by deep learning-based LFI angular reconstruction and spatial super-resolution methods, individually and multiplely. Besides, in the case of multiple distortions, the adoption order of two distortions is taken into consideration. Further, our database presents three types of LFIs that suffer from distortions: real-world, dense synthesis, and sparse synthesis. As a result, the quality of distorted LFIs was subjectively assessed by 32 valid observers using the Pairwise Comparison (PC) protocol. Secondly, we develop a novel objective No-Reference (NR) metric for LFI quality evaluation, based on the features extracted from spatial gradients, angular-spatial statistics, and binocular disparity. Finally, a benchmark of the proposed metric and numerous state-of-the-art quality assessment metrics on the proposed database is presented. Experimental results demonstrate the superiority of the proposed metric over most existing metrics in various aspects. The proposed database and metric will be publicly available at https://github.com/ZhengyuZhang96/IETR-LFI.