AESTHETIC-DRIVEN IMAGE COMPOSITION ENHANCEMENT : A SURVEY

Chao Fan

Abstract


Composition has a great impact on image. With the development of smart phone, personal digital camera and the social sharing platforms such as Instagram, tons of photos are taken and shared by users. Everyone wants to take beautiful pictures. For professional photographers, due to their professional knowledge, such as rule of third, visual balance, diagonal dominance, they know how to adjust their camera and apply their knowledge to take aesthetically pleasing images. However, for amateurs, because of the lack of professional photographic composition knowledge, they usually don’t know how to take a beautiful image. Many factors lead to aesthetically pleasing photos, one of them is composition. Good composition leads to visually pleasing image. In the recent years, image composition enhancement technics has drawn great attention to the researchers by employing the professional composition knowledge. Many methods have been proposed to improve the composition of the images, i.e., image retargeting, warping, seam carving, image cropping. Due to the popularity of deep learning, some AI technics have applied into the Image composition enhancement methods. In this paper, we present a survey of state-of-the-arts aesthetic-driven composition enhancement technics. In this paper, We first introduce the problem of the image composition enhancement problem, then we discuss the development of the computational aesthetics, next we present a comprehensive review of different image composition methods. At last, we give a conclusion after analyzing each method and present a research direction for the future. This survey intends to give the researchers a reference who wants to set foot in the field of image composition enhancement.

Keywords


Image enhancement , Image re-composition, Image aesthetics, composition rules, Computer vision

Full Text:

PDF

References


Bhattacharya, S., Sukthankar, R., & Shah, M. (2010). A framework for photo-quality assessment and enhancement based on visual aesthetics. Paper presented at the acm multimedia.

Chang, H., Pan, P., Wang, Y. F., & Chen, M. (2015). R2P: Recomposition and Retargeting of Photographic Images. Paper presented at the acm multimedia.

Chang, K., Lu, K., & Chen, C. (2017). Aesthetic Critiques Generation for Photos. Paper presented at the international conference on computer vision.

Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2006). Studying aesthetics in photographic images using a computational approach. Paper presented at the european conference on computer vision.

Deng, Y., Loy, C. C., & Tang, X. (2017). Aesthetic-Driven Image Enhancement by Adversarial Learning. arXiv: Computer Vision and Pattern Recognition.

Guo, Y. W., Liu, M., Gu, T. T., & Wang, W. P. (2012). Improving Photo Composition Elegantly: Considering Image Similarity During Composition Optimization. Computer Graphics Forum, 31(7), 2193-2202. doi:10.1111/j.1467-8659.2012.03212.x

Gupta, A., Johnson, J., Alahi, A., & Feifei, L. (2017). Characterizing and Improving Stability in Neural Style Transfer. Paper presented at the international conference on computer vision.

Huang, X., & Belongie, S. (2017). Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization. Paper presented at the international conference on computer vision.

Islam, M. B., Lai-Kuan, W., & Chee-Onn, W. (2016). A survey of aesthetics-driven image recomposition. Multimedia Tools and Applications, 76(7), 9517-9542. doi:10.1007/s11042-016-3561-5

Jeong, K., & Cho, H.-J. (2015). A Digitalized Recomposition Technique Based on Photo Quality Evaluation Criteria. Wireless Personal Communications, 86(1), 301-314. doi:10.1007/s11277-015-2977-y

Jin, X., Chi, J., Peng, S., Tian, Y., Ye, C., & Li, X. (2016). Deep image aesthetics classification using inception modules and fine-tuning connected layer. Paper presented at the international conference on wireless communications and signal processing.

Johnson, J., Alahi, A., & Feifei, L. (2016). Perceptual Losses for Real-Time Style Transfer and Super-Resolution. Paper presented at the european conference on computer vision.

Kong, S., Shen, X., Lin, Z., Mech, R., & Fowlkes, C. C. (2016). Photo Aesthetics Ranking Network with Attributes and Content Adaptation. Paper presented at the european conference on computer vision.

Lee, J., Sunkavalli, K., Lin, Z., Shen, X., & Kweon, I. S. (2016). Automatic Content-Aware Color and Tone Stylization. Paper presented at the computer vision and pattern recognition.

Li, D., Wu, H., Zhang, J., & Huang, K. (2019). Fast A3RL: Aesthetics-Aware Adversarial Reinforcement Learning for Image Cropping. IEEE Transactions on Image Processing, 28(10), 5105-5120.

Liu, D. S.-M., & Huang, C.-C. (2017). Optimizing Aesthetic-Based Photo Retargeting. In Smart Graphics (pp. 41-60).

Liu, L., Chen, R., Wolf, L., & Cohenor, D. (2010). Optimizing Photo Composition. Computer Graphics Forum, 29(2), 469-478.

Lu, X., Lin, Z., Jin, H., Yang, J., & Wang, J. Z. (2014). RAPID: Rating Pictorial Aesthetics using Deep Learning. Paper presented at the acm multimedia.

Ma, N., Volkov, A., Livshits, A., Pietrusinski, P., Hu, H., & Bolin, M. (2019). An Universal Image Attractiveness Ranking Framework. Paper presented at the workshop on applications of computer vision.

Murray, N., Marchesotti, L., & Perronnin, F. (2012). AVA: A large-scale database for aesthetic visual analysis. Paper presented at the computer vision and pattern recognition.

Ni, B., Xu, M., Cheng, B., Wang, M., Yan, S., & Tian, Q. (2013). Learning to Photograph: A Compositional Perspective. IEEE Transactions on Multimedia, 15(5), 1138-1151. doi:10.1109/tmm.2013.2241042

Park, J., Lee, J., Tai, Y., & Kweon, I. S. (2012). Modeling photo composition and its application to photo re-arrangement. Paper presented at the international conference on image processing.

Riaz, S., Park, U., & Lee, S.-W. (2015). A photograph reconstruction by object retargeting for better composition. Multimedia Tools and Applications, 75(23), 16439-16460. doi:10.1007/s11042-015-3037-z

Sun, W., Chao, T., Kuo, Y., & Hsu, W. H. (2017). Photo Filter Recommendation by Category-Aware Aesthetic Learning. IEEE Transactions on Multimedia, 19(8), 1870-1880.

Tong, H., Li, M., Zhang, H., He, J., & Zhang, C. (2004). Classification of digital photos taken by photographers or home users. Paper presented at the advances in multimedia.

Wang, W., Yang, S., Zhang, W., & Zhang, J. (2018). Neural Aesthetic Image Reviewer. arXiv: Computer Vision and Pattern Recognition.

Wang, W. N., Liu, J. C., Xu, X. M., Jiang, Y. Z., & Wang, L. (2015). Aesthetic Enhancement of Images Based on Photography Composition Guidelines. Journal of South China University of Technology(Natural ence Edition), 43(5), 51-58.

Wang, Z., Liu, D., Chang, S., Dolcos, F., Beck, D. M., & Huang, T. S. (2017). Image aesthetics assessment using Deep Chatterjee's machine. Paper presented at the international joint conference on neural network.

Wong, L., & Wong, K. (2012). Enhancing visual dominance by semantics-preserving image recomposition. Paper presented at the acm multimedia.

Yan, J., Lin, S., Kang, S. B., & Tang, X. (2014). A Learning-to-Rank Approach for Image Color Enhancement. Paper presented at the computer vision and pattern recognition.

Zhang, F.-L., Wang, M., & Hu, S.-M. (2013). Aesthetic Image Enhancement by Dependence-Aware Object Recomposition. IEEE Transactions on Multimedia, 15(7), 1480-1490. doi:10.1109/tmm.2013.2268051

Zhang, X., Constable, M., Chan, K. L., Yu, J., & Junyan, W. (2018). Composition Improvement for Portrait Photographs. In Computational Approaches in the Transfer of Aesthetic Values from Paintings to Photographs (pp. 137-153).


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2019 International Educational Applied Scientific Research Journal