Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models

Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE TIP (2004)

2004

Earlier work this paper cites.

Sheikh, H.: Live image quality assessment database release 2. http://live.ece.utexas.edu/research/quality (2005)

2005

Earlier work this paper cites.

Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE TIP (2006)

2006

Earlier work this paper cites.

Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: Tid2008-a database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics (2009)

2009

Earlier work this paper cites.

Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging (2010)

2010

Earlier work this paper cites.

Moorthy, A.K., Bovik, A.C.: A two-step framework for constructing blind image quality indices. IEEE Sign. Process. Letters (2010)

2010

Earlier work this paper cites.

Moorthy, A.K., Bovik, A.C.: Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE TIP (2011)

2011

Earlier work this paper cites.

Tang, H., Joshi, N., Kapoor, A.: Learning a blind measure of perceptual image quality. In: CVPR (2011)

2011

Earlier work this paper cites.

Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: A feature similarity index for image quality assessment. IEEE TIP (2011)

2011

Earlier work this paper cites.

Jayaraman, D., Mittal, A., Moorthy, A.K., Bovik, A.C.: Objective quality assessment of multiply distorted images. In: Asilomar Conference on Signals, Systems and Computers (ASILOMAR) (2012)

2012

Earlier work this paper cites.

Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE TIP (2012)

2012

Earlier work this paper cites.

Saad, M.A., Bovik, A.C., Charrier, C.: Blind image quality assessment: A natural scene statistics approach in the dct domain. IEEE TIP (2012)

2012

Earlier work this paper cites.

Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Sign. Process. Letters (2013)

2013

Earlier work this paper cites.

Kang, L., Ye, P., Li, Y., Doermann, D.: Convolutional neural networks for no-reference image quality assessment. In: CVPR (2014)

2014

Earlier work this paper cites.

Young, P., Lai, A., Hodosh, M., Hockenmaier, J.: From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. Transactions of the Association for Computational Linguistics (2014)

2014

Earlier work this paper cites.

Bell, S., Upchurch, P., Snavely, N., Bala, K.: Material recognition in the wild with the materials in context database. In: CVPR (2015)

2015

Earlier work this paper cites.

Chen, X., Fang, H., Lin, T.Y., Vedantam, R., Gupta, S., Dollár, P., Zitnick, C.L.: Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325 (2015)

Original

2015

Earlier work this paper cites.

Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., et al.: Image database tid2013: Peculiarities, results and perspectives. Signal Processing: Image Communication (2015)

2015

Earlier work this paper cites.

Bosse, S., Maniry, D., Müller, K.R., Wiegand, T., Samek, W.: Deep neural networks for no-reference and full-reference image quality assessment. IEEE TIP (2017)

2017

Earlier work this paper cites.

Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., Parikh, D.: Making the V in VQA matter: Elevating the role of image understanding in visual question answering. In: CVPR (2017)

2017

Earlier work this paper cites.

Liu, X., van de Weijer, J., Bagdanov, A.D.: RankIQA: Learning from rankings for no-reference image quality assessment. In: ICCV (2017)

2017

Earlier work this paper cites.

Ma, C., Yang, C.Y., Yang, X., Yang, M.H.: Learning a no-reference quality metric for single-image super-resolution. Computer Vision and Image Understanding (2017)

2017

Earlier work this paper cites.

Sun, W., Zhou, F., Liao, Q.: Mdid: A multiply distorted image database for image quality assessment. PR (2017)

2017

Earlier work this paper cites.

Bosse, S., Maniry, D., Müller, K.R., Wiegand, T., Samek, W.: Deep neural networks for no-reference and full-reference image quality assessment. IEEE TIP (2018)

2018

Earlier work this paper cites.

Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

Original

2018

Earlier work this paper cites.

Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. In: EMNLP (2018)

2018

Earlier work this paper cites.

Pan, D., Shi, P., Hou, M., Ying, Z., Fu, S., Zhang, Y.: Blind predicting similar quality map for image quality assessment. In: CVPR (2018)

2018

Earlier work this paper cites.

Prashnani, E., Cai, H., Mostofi, Y., Sen, P.: Pieapp: Perceptual image-error assessment through pairwise preference. In: CVPR (2018)

2018

Earlier work this paper cites.

Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)

2018

Cited alongside, same era.

Agrawal, H., Desai, K., Wang, Y., Chen, X., Jain, R., Johnson, M., Batra, D., Parikh, D., Lee, S., Anderson, P.: Nocaps: Novel object captioning at scale. In: ICCV (2019)

2019

Cited alongside, same era.

Lin, H., Hosu, V., Saupe, D.: Kadid-10k: A large-scale artificially distorted iqa database. In: International Conference on Quality of Multimedia Experience (QoMEX) (2019)

2019

Cited alongside, same era.

Singh, A., Natarajan, V., Shah, M., Jiang, Y., Chen, X., Batra, D., Parikh, D., Rohrbach, M.: Towards VQA models that can read. In: CVPR (2019)

2019

Cited alongside, same era.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. In: NeurIPS (2020)

Chen, Z., He, Z., Lu, Z., Lu, Z.M.: Prompt-based test-time real image dehazing: a novel pipeline. arXiv preprint arXiv:2309.17389 (2023)

Original

2023

Closest in time.

Chiang, W.L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., et al.: Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. https://vicuna.lmsys.org (2023)

2023

Closest in time.

Dai, W., Li, J., Li, D., Tiong, A.M.H., Zhao, J., Wang, W., Li, B., Fung, P.N., Hoi, S.: InstructBLIP: Towards general-purpose vision-language models with instruction tuning. In: NeurIPS (2023)

2023

Closest in time.

Huang, S., Dong, L., Wang, W., Hao, Y., Singhal, S., Ma, S., Lv, T., Cui, L., Mohammed, O.K., Liu, Q., et al.: Language is not all you need: Aligning perception with language models. arXiv preprint arXiv:2302.14045 (2023)

Original

2023

Closest in time.