Conference Paper

Photo and Video Quality Evaluation: Focusing on the Subject.

DOI: 10.1007/978-3-540-88690-7_29 Conference: Computer Vision - ECCV 2008, 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part III
Source: DBLP


Traditionally, distinguishing between high quality professional pho- tos and low quality amateurish photos is a human task. To automatically assess the quality of a photo that is consistent with humans perception is a challenging topic in computer vision. Various differences exist between photos taken by pro- fessionals and amateurs because of the use of photography techniques. Previous methods mainly use features extracted from the entire image. In this paper, based on professional photography techniques, we first extract the subject region from a photo, and then formulate a number of high-level semantic features based on this subject and background division. We test our features on a large and diverse photo database, and compare our method with the state of the art. Our method performs significantly better with a classification rate of 93% versus 72% by the best existing method. In addition, we conduct the first study on high-level video quality assessment. Our system achieves a precision of over 95% in a reason- able recall rate for both photo and video assessments. We also show excellent application results in web image search re-ranking.

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Available from: Xiaoou Tang, Feb 22, 2015
    • "Video quality and enjoyment evaluation has been widely investigated in the last few years, and numerous related video databases have been constructed. For example, the: VQEG HDTV database [13]; LIVE video database [36]; IVC video databases [23]; ReTRiEVED video database [31]; video enjoyment database [24], and aesthetic evaluation video database [29]. In these video quality assessment databases, the visual quality of videos derived from different distortion types (e.g., H.264 compression, packet loss and frame rate change) is evaluated by human beings. "
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    ABSTRACT: Perception of multimedia quality is shaped by a rich interplay between system, context, and human factors. While system and context factors are widely researched, few studies in this area consider human factors as sources of systematic variance. This paper presents an analysis on the influence of personality (Five-Factor Model) and cultural traits (Hofstede Model) on the perception of multimedia quality. A set of 144 video sequences (from 12 short movie excerpts) were rated by 114 participants from a cross-cultural population, producing 1232 ratings. On this data, three models are compared: a baseline model that only considers system factors; an extended model that includes personality and culture as human factors; and an optimistic model in which each participant is modeled as a random effect. An analysis shows that personality and cultural traits represent 9.3% of the variance attributable to human factors while human factors overall predict an equal or higher proportion of variance compared to system factors. In addition, the quality-enjoyment correlation varied across the movie excerpts. This suggests that human factors play an important role in perceptual multimedia quality, but further research to explore moderation effects and a broader range of human factors is warranted.
    Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia; 10/2015
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    • "Most of recent works perform region of interest (ROI) extraction to enhance their prediction results since different objects locations, shapes or color compositions may change the global aesthetic quality of an image (Datta et al., 2006). ROI may be detected using sharpness estimation (Luo and Tang, 2008), saliency maps (Wong and Low, 2009; Tong et al., 2010) or object detection (Viola and Jones, 2001). "
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    ABSTRACT: An automated system that provides feedback about aesthetic quality of facial pictures could be of great interest for editing or selecting photos. Although image aesthetic quality assessment is a challenging task that requires understanding of subjective notions, the proposed work shows that facial image quality can be estimated by using low-level features only. This paper provides a method that can predict aesthetic quality scores of facial images. 15 features that depict technical aspects of images such as contrast, sharpness or colorfulness are computed on different image regions (face, eyes, mouth) and a machine learning algorithm is used to perform classification and scoring. Relevant features and facial image areas are selected by a feature ranking technique, increasing both classification and regression performance. Results are compared with recent works, and it is shown that by using the proposed low-level feature set, the best state of the art results are obtained. Copyright © 2015 SCITEPRESS - Science and Technology Publications All rights reserved.
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    • "A notable exception and closely related to our work is the work by Jiang et al. [4], in which one of the three main criteria that characterize a salient object is that " it is most probably placed near the center of the image " [4]. The authors justify this characterization with the " rule of thirds " , which is one of the most well-known principles of photographic composition (see, e.g., [17]), and use a Gaussian distance metric as a model. We go beyond following the rule of third and show that the distribution of the objects' centroids correlates strongly positively with a 2-dimensional Gaussian distribution. "
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    ABSTRACT: It has become apparent that a Gaussian center bias can serve as an important prior for visual saliency detection, which has been demonstrated for predicting human eye fixations and salient object detection. Tseng et al. have shown that the photographer's tendency to place interesting objects in the center is a likely cause for the center bias of eye fixations. We investigate the influence of the photographer's center bias on salient object detection, extending our previous work. We show that the centroid locations of salient objects in photographs of Achanta and Liu's data set in fact correlate strongly with a Gaussian model. This is an important insight, because it provides an empirical motivation and justification for the integration of such a center bias in salient object detection algorithms and helps to understand why Gaussian models are so effective. To assess the influence of the center bias on salient object detection, we integrate an explicit Gaussian center bias model into two state-of-the-art salient object detection algorithms. This way, first, we quantify the influence of the Gaussian center bias on pixel- and segment-based salient object detection. Second, we improve the performance in terms of F1 score, Fb score, area under the recall-precision curve, area under the receiver operating characteristic curve, and hit-rate on the well-known data set by Achanta and Liu. Third, by debiasing Cheng et al.'s region contrast model, we exemplarily demonstrate that implicit center biases are partially responsible for the outstanding performance of state-of-the-art algorithms. Last but not least, as a result of debiasing Cheng et al.'s algorithm, we introduce a non-biased salient object detection method, which is of interest for applications in which the image data is not likely to have a photographer's center bias (e.g., image data of surveillance cameras or autonomous robots).
    PLoS ONE 01/2015; 10(7). DOI:10.1371/journal.pone.0130316 · 3.23 Impact Factor
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