Memorability measures how easily an image is to be memorized after glancing, which may contribute to designing magazine covers, tourism publicity materials, and so forth. Recent works have shed light on the visual features that make generic images, object images or face photographs memorable. However, these methods are not able to effectively predict the memorability of outdoor natural scene images. To overcome this shortcoming of previous works, in this paper, we provide an attempt to answer: “what exactly makes outdoor natural scenes memorable”. To this end, we first establish a large-scale outdoor natural scene image memorability (LNSIM) database, containing 2,632 outdoor natural scene images with their ground truth memorability scores and the multi-label scene category annotations. Then, similar to previous works, we mine our database to investigate how low-, middle- and high-level handcrafted features affect the memorability of outdoor natural scenes. In particular, we find that the high-level feature of scene category is rather correlated with outdoor natural scene memorability, and the deep features learnt by deep neural network (DNN) are also effective in predicting the memorability scores. Moreover, combining the deep features with the category feature can further boost the performance of memorability prediction. Therefore, we propose an end-to-end DNN based outdoor natural scene memorability (DeepNSM) predictor, which takes advantage of the learned category-related features. Then, the experimental results validate the effectiveness of our DeepNSM model, exceeding the state-of-the-art methods. Finally, we try to understand the reason of the good performance for our DeepNSM model, and also study the cases that our DeepNSM model succeeds or fails to accurately predict the memorability of outdoor natural scenes.

Different from 2-dimensional (2D) videos, panoramic videos contain spherical viewing direction with the support of head-mounted displays, thus improving immersive and interactive visual experience. Unfortunately, to our best knowledge, there are few subjective visual quality assessment (VQA) methods for panoramic videos. In this paper, we therefore propose a subjective VQA method for assessing quality loss of impaired panoramic videos. Specifically, we first establish a database containing viewing direction data of several subjects on watching panoramic videos. Then, we find out that there exists high consistency of viewing direction on panoramic videos across different subjects. Upon this finding, we present a procedure of subjective test in measuring quality of panoramic videos by different subjects, yielding different mean opinion score (DMOS). To couple with inconsistency of viewing directions on panoramic videos, we further propose a vectorized DMOS metric. Finally, experimental results verify that our subjective VQA method, in the forms of both overall and vectorized DMOS metrics, is effective in measuring subjective quality of panoramic videos.

Recent work has shown that image memorability, in general, can be reliably predicted using some state-of-the-art features. However, all existing methods are not effective in predicting memorability of natural-scene images, far from human. In this paper, we propose a novel method to improve the effectiveness of memorability prediction for natural-scene images. Specifically, we argue that some of HSV colors have either positive or negative impact on memorability of natural-scene images in our Natural-Scene Image Memorability (NSIM) dataset. Then, we develop an HSV-based feature for memorability prediction. Finally, the HSV-based feature is combined with other efficient state-of-the-art features in our approach to predict memorability on natural-scene images. Experimental results validate the effectiveness of our method.