No-Reference Quality Assessment of In-Capture Distorted Videos

Abstract

We introduce a no-reference method for the assessment of the quality of videos affected by in-capture distortions due to camera hardware and processing software. The proposed method encodes both quality attributes and semantic content of each video frame by using two Convolutional Neural Networks (CNNs) and then estimates the quality score of the whole video by using a Recurrent Neural Network (RNN), which models the temporal information. The extensive experiments conducted on four benchmark databases (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC) containing in-capture distortions demonstrate the effectiveness of the proposed method and its ability to generalize in cross-database setup.

An Efficient Method for No-Reference Video Quality Assessment

“…This paper extends our previous work [ 5 ] in three aspects: (1) ResNet-50 [ 10 ] architectures exploited in the Multi-level feature extraction module are replaced by the more efficient and lightweight MobileNet-v2 [ 11 ], which improves the efficiency without sacrificing performance. (2) Frame-level features are mapped to a video quality score thanks to the Video quality estimation module which is much more simple than the Temporal modeling module.…”

“…The automatic estimation of the quality of a UGC as perceived by human observers is fundamental for a wide range of applications. For example, to discriminate professional and amateur video content on user-generated video distribution platforms [ 1 ], to choose the best sequence among many sequences for sharing in social media [ 2 ], to guide a video enhancement process [ 3 ], and to rank/choose user-generated videos [ 4 , 5 ].…”

“…For this reason, methods designed on synthetically distorted video datasets do not scale on natural distortions and more effort needs to be made to develop datasets and methods suitable for NR-VQA in the wild. Recently, some quality databases with UGC videos affected by natural distortions have been published [ 6 , 7 , 8 ] and the focus of blind video quality prediction methods has gradually shifted to techniques capable of detecting natural video distortions as well [ 4 , 5 , 9 ]. Although important steps forward have been made from the point of view of the effectiveness of NR-VQA methods on videos in-the-wild, another important aspect that needs to be investigated concerns the efficiency of the proposed methods, especially if they have to be deployed on embedding devices with limited resources such as smartphones and tablets.…”

“…In this paper, we mainly focus on the problem of efficiently assessing the quality of in-the-wild videos. The proposed method relies on the insight of our previous article that the combination of quality features with semantic features produces video quality scores in close agreement with human judgment [ 5 ]. In this work, we take up the idea of encoding video frames in terms of both semantics and quality and we design a lightweight and efficient method.…”

“…In this work, we take up the idea of encoding video frames in terms of both semantics and quality and we design a lightweight and efficient method. The proposed method includes a Multi-level feature extraction module as presented in [ 5 ]. This consists of two lightweight Convolutional Neural Networks (CNNs) for encoding each video frame in terms of both semantic and quality features.…”