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A vehicle detection method fusing spatial saliency and local image features is presented in this paper to analyze the detection performance in compressed domain. Most of the existing visual saliency models use the input images, in which salient objects are to be detected and are free from complex background and overlapping areas. Moreover, they are very sensitive to the complex scene and different illuminations. In this method, the Scale Invariant Feature Transform and Harris features in combination with spatial saliency model play an important role in detecting vehicles from the scene. A one‐to‐one symmetric search is performed on the descriptors to select a set of matched interest point pairs for vehicle detection. We use 4K video of a road scene with different types of vehicles. The proposed method is able to detect desired overlapping objects from the road scene without heavy computation like other training‐based methods. In addition, the detection performance is analyzed in a H.265 compressed domain to help construct a relationship between objective and subjective evaluation value. The performance of this method is demonstrated by the experimental results. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Visual quality of compressed video sequences depends on factors including spatial texture content and cognition-based factors such as prior knowledge and task in hand. The MOSp metric is a full reference objective quality metric which predicts perceived quality of sequences with video compression-induced impairments based on the spatial texture content and the mean squared error between original and compressed video sequences. In this paper, we extend the MOSp metric to incorporate cognition-based factors to identify regions in a video scene that attract human attention. The proposed metric has been tested on a variety of multimedia sequences of common intermediate format resolution compressed at a wide range of bitrates using the H.264/AVC coding standard. This metric shows a higher correlation with mean opinion score (MOS) than popular metrics, such as peak signal-to noise ratio, National Telecommunications and Information Administration/Institute for Telecommunication Sciences video quality metric, PSNRplus, and the Yonsei University metric. Results also show that by extending the MOSp metric to incorporate cognition-based factors such as skin information, its correlation with subjective scores (MOS) can be significantly improved in video content where humans are present. This algorithm is particularly useful for real-time quality estimation of multimedia sequences with block-based video compression-induced impairments because all the parameters of the metric can be calculated automatically with a modest amount of processing overhead. [ABSTRACT FROM PUBLISHER]