Photo and Video Quality Evaluation: Focusing on the Subject

By Yiwen Luo, Xiaoou Tang. European Conference on Computer Vision, 2008

503 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8b2b290182d69d2160d727d7ecb10e9ff4587937

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In Computer Vision and Pattern Recognition

Citations in the corpus

Content-based photo quality assessment (2011, methodology): “The comparison of the method in [12] and ours can be found in Figure 3.”
Photo Aesthetics Ranking Network with Attributes and Content Adaptation (2016, methodology): “Datasets such as CUHKPQ [11,19] only provide binary labels (low or high aesthetics) which cannot easily be used for rating prediction.”
Image Aesthetic Assessment: An experimental survey (2016, methodology): “Regional image features [13], [14], [15] later prove to be effective in complementing the global features.”
Automatic salient object segmentation based on context and shape prior (2011, background): “The second one as a location prior, has been studied in photo quality assessment [4, 8, 20], known as Rule of Thirds.”
Learning beautiful (and ugly) attributes (2013, background): “Many subsequent works have focused on adding new photographic rules and on improving the visual features of existing rules [7, 17].”
Learning photographic global tonal adjustment with a database of input/output image pairs (2011, background): “[5, 12], have been proposed to assess the visual quality of photos.”
The Synthesizability of Texture Examples (2014, background): “Even if challenging, the task may be doable, given that other qualitative image characteristics could be quantified, like quality [26], memorability [14], or interestingness [11].”
What Makes a Photograph Memorable? (2014, unknown): “Index Terms—Scene understanding, image memorability, global image features, attributes ✦”
Memorability of Image Regions (2012, background): “Recently, learning algorithms have been proposed to automatically interpret whether an image is aesthetically pleasant or not [20, 3], memorable or forgettable [7, 6], and the role that other high level photographic properties plays in image interpretation (photo quality [19], attractiveness [16], c”
AVA: A large-scale database for aesthetic visual analysis (2012, background): “Typically, features capturing the “aes-thetic” properties of an image are proposed with the aim of mimicking photographic rules and practices such as the golden ratio, the rule of thirds and color harmonies [3, 11, 16, 6, 15].”
Adopting Abstract Images for Semantic Scene Understanding (2016, background): “Many other photographic properties have been studied in the literature such as photo quality [50], saliency [1], attractiveness [51], composition [52], [53], color harmony [54], aesthetics [55] and object importance [5].”
A framework for photo-quality assessment and enhancement based on visual aesthetics (2010, methodology): “Thus, in order to understand the nuances of spatial composition in photographic frames, the authors of [16] additionally introduced a parameter that considered adherence to geometric composition rules for photo and video quality evaluation.”
High level describable attributes for predicting aesthetics and interestingness (2011, background): “Compositional Attributes - characteristics related to the layout of an image that indicate how closely the image follows photographic rules of composition.”
RAPID: Rating Pictorial Aesthetics using Deep Learning (2014, background): “Various handcrafted aesthetics-relevant features have been proposed [6, 13, 21, 3, 20, 7, 26, 27], including low-level image statistics such as distributions of edges and color histograms, and high-level photographic rules such as the rule of thirds.”
Exploring Principles-of-Art Features For Image Emotion Recognition (2014, methodology): “Based on professional photography techniques, Luo and Tang [25] extracted the subject region from a photo and formulated a number of high-level semantic features based on this subject and background division.”
Assessing the aesthetic quality of photographs using generic image descriptors (2011, methodology): “For the segmentation, a simple k-means region segmentation could be used [4], while more complex methods can spot in focus objects by detecting blurry edges (corresponding to background) [17].”