Content-based photo quality assessment

By Wei Luo, Xiaogang Wang, Xiaoou Tang. Vision, 2011

456 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/627ab487cbf0bf0062b318da37b479d571957ff7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Luc Van Gool
3. Ming-Hsuan Yang
4. Wenguan Wang
5. Chen Change Loy
6. Jianbing Shen
7. Haibin Ling
8. Yi Yang

In Media Technology

1. Ke Gu
2. Weisi Lin
3. Guangtao Zhai
4. Xiaokang Yang
5. Dacheng Tao
6. J. Qiao
7. Luca Marchesotti
8. Wenjun Zhang

Citations in the corpus

• Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data (2018, methodology): “We have first collected 1642 images that contain 1242 natural scene images coming from Berkeley database [57] and high-quality subsets in PQD database [58] as well as 400 screen content images captured by ourselves with a screenshot tool.”
• AVA: A large-scale database for aesthetic visual analysis (2012, methodology): “We selected 8 semantic categories equivalent to the ones picked by [15].”
• No-Reference Image Sharpness Assessment in Autoregressive Parameter Space (2015, methodology): “The selected ten high-quality images from the CUHKPQ image database [55].”
• 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.”
• A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping (2019, methodology): “Some works [44], [45], [46], [47], [48] use hand-crafted aesthetics”
• Photo Aesthetics Ranking Network with Attributes and Content Adaptation (2016, background): “While judging image aesthetics is a subjective task, it has been an area of active study in recent years and substantial progress has been made in identifying and quantifying those image features that are predictive of favorable aesthetic judgements by most individuals [20,17,16,18,5].”