NTIRE 2021 Perceptual Image Quality Assessment Challenge

Organized by Radu - Current server time: Jan. 25, 2021, 11:43 a.m. UTC

Current

Development
Jan. 1, 2021, 11:59 p.m. UTC

Next

Testing
March 1, 2021, 11:59 p.m. UTC

End

Competition Ends
March 8, 2021, 11:59 p.m. UTC

NTIRE Workshop and Challenges @ CVPR 2021

 

Perceptual Image Quality Assessment Challenge

Important dates

  • 2021.01.10 Release of train data (input and output) and validation data (inputs only)
  • 2021.01.23 Validation server online
  • 2021.03.01 Final test data release (inputs only)
  • 2021.03.08 Test output results submission deadline
  • 2021.03.09 Fact sheets and code/executable submission deadline
  • 2021.03.11 Preliminary test results release to the participants
  • 2021.03.28 Paper submission deadline for entries from the challenge
  • 2021.06.15 NTIRE workshop and challenges, results and award ceremony (CVPR 2021, Online)

Competition Updates

  • Jan. 23rd: NEW! The validation data is already made available to the registered participants. The validation server is now online. Submission instructions can be found on the Evaluation page.
  • Jan. 10th: The training data is already made available to the registered participants. All the participants will be approved to access the training data. The validation server will go online soon. Before the verification server goes online, verification submission is temporarily unavailable.

Challenge overview

The 6th edition of NTIRE: New Trends in Image Restoration and Enhancement workshop will be held in June 2021 in conjunction with CVPR 2021.

Image manipulation is a key computer vision tasks, aiming at the restoration of degraded image content, the filling in of missing information, or the needed transformation and/or manipulation to achieve a desired target (with respect to perceptual quality, contents, or performance of apps working on such images). Recent years have witnessed an increased interest from the vision and graphics communities in these fundamental topics of research. Not only has there been a constantly growing flow of related papers, but also substantial progress has been achieved.

Each step forward eases the use of images by people or computers for the fulfilment of further tasks, as image manipulation serves as an important frontend. Not surprisingly then, there is an ever-growing range of applications in fields such as surveillance, the automotive industry, electronics, remote sensing, or medical image analysis etc. The emergence and ubiquitous use of mobile and wearable devices offer another fertile ground for additional applications and faster methods.

This workshop aims to provide an overview of the new trends and advances in those areas. Moreover, it will offer an opportunity for academic and industrial attendees to interact and explore collaborations.

Motivation

The Image Quality Assessment (IQA) methods are developed to measure the perceptual quality of images after degradation or post-processing operation. Although it is nearly effortless for human eyes to distinguish perceptually better images, it is challenging for an algorithm to measure visual quality fairly. One of the most important applications of IQA is to measure the performance of image restoration algorithms. To some extent, these IQA methods are the chief reason for the considerable progress of the image restoration field. However, while new algorithms have been continuously improving image restoration performance, we notice an increasing inconsistency between quantitative results and perceptual quality. Especially, the invention of Generative Adversarial Networks (GAN) and GAN-based image restoration algorithms poses a great challenge for IQA, as they bring completely new characteristics to the output images. This also affected the development in the field of image restoration, as comparing them with the flawed IQA methods may not lead to better perceptual quality. With the development of image restoration technology, new IQA methods need to be proposed accordingly.

Jointly with NTIRE workshop, we have an NTIRE challenge on perceptual image quality assessment, that is, the task of predicting the perceptual quality of an image based on a set of prior examples of images and their perceptual quality labels. The aim is to obtain a network design/solution capable to produce high-quality results with the best correlation to the reference ground truth MOS score.

The challenge uses a new dataset called PIPAL [1] and has a single track. PIPAL training set contains 200 reference images, 40 distortion types, 23k distortion images, and more than one million human ratings. Especially, we include GAN-based algorithms’ outputs as a new GAN-based distortion type. We employ the Elo rating system to assign the Mean Opinion Scores (MOS). An extension set of PIPAL will be collected for testing, which follows the same rating procedure with PIPAL. More details are found in the data section of the competition.

[1] Gu, Jinjin, et al. "PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration." ECCV. 2020. [link] 

The top-ranked participants will be awarded and invited to follow the CVPR submission guide for workshops to describe their solution and to submit to the associated NTIRE workshop at CVPR 2021.

Provided Resources

  • Scripts: With the dataset, the organizers will provide scripts to facilitate the reproducibility of the images and performance evaluation results after the validation server is online. More information is provided on the data page.
  • Contact: You can use the forum on the data description page (highly recommended!) or directly contact the challenge organizers by email (Jinjin Gu <jinjin.gu@sydney.edu.au>, Jimmy S. Ren <rensijie@sensetime.com>, Chao Dong <chao.dong@siat.ac.cn>, Shuhang Gu <shuhanggu@gmail.com>, and Radu Timofte ) if you have doubts or any question.

NTIRE Workshop and Challenges @ CVPR 2021

 

Perceptual Image Quality Assessment Challenge

 

Evaluation

The evaluation consists from the comparison of the predictions with the reference ground truth Mean Opinion Scores (MOS).

We use the Pearson linear correlation coefficient (PLCC) and Spearman rank-order correlation coefficients (SROCC) as often employed in the literature. The Pearson coefficient is used to evaluate the accuracy of the methods and the Spearman coefficient is used to evaluate the monotonicity of the methods. Before calculating the PLCC index, we perform the third-order polynomial nonlinear regression. For each dataset, we report the results over all the processed images belonging to it. We ignore the sign and only report the absolute values.

Their implementations are found in most of the statistics/machine learning toolboxes. For example, the demo evaluation code in MATLAB:

% SROCC
r_s = abs(corr(IQA_scores_predicted, MOS_scores, 'type', 'Spearman'));

% PLCC with Three order polynomial
fit_func = polyfit(IQA_scores_predicted, MOS_scores, 3);
fitted_MOS = polyval(fit_func, IQA_scores_predicted);
r_p = abs(corr(fitted_MOS, MOS_scores, 'type', 'Pearson'));

Submission

For submitting the results, you need to follow these steps:

  1. Process the distortion images and record the predicted scores in a single output file named output.txt (must in this name). Each line in this file records the image name and the corresponding scores. The order should not be modified, you can refer to the demo submission file or obtain the image order by sorted() function in Python. Here is a demo output.txt format:
    A0000_00_00.bmp,0.193684
    A0000_00_01.bmp,0.301328
    A0000_00_02.bmp,0.158600
    A0000_00_03.bmp,0.276821
    A0000_00_04.bmp,0.353658
    A0000_00_05.bmp,0.165624
    A0000_00_06.bmp,0.274205
    A0000_00_07.bmp,0.353495
    A0000_00_08.bmp,0.167050
    A0000_00_09.bmp,0.281876
    A0000_00_10.bmp,0.364449
    A0000_00_11.bmp,0.151699
    A0000_01_00.bmp,0.142952
    A0000_01_01.bmp,0.243252
    A0000_01_02.bmp,0.313606
    ...
  2. Create a readme.txt (must in this name) that should contain the following lines filled in with the runtime per image (in seconds) of the solution, 1 or 0 accordingly if employs CPU or GPU at runtime, 1 or 0 if employs extra data for training the models or not, and 1 or 0 if the contributed method is full-reference or not.
    runtime per image [s] : 10.43
    CPU[1] / GPU[0] : 1
    Extra Data [1] / No Extra Data [0] : 1
    Other description : Solution based on A+ of Timofte et al. ACCV 2014. We have a Matlab/C++ implementation, and report single core CPU runtime. The method was trained on Train 91 of Yang et al. and BSDS 200 of the Berkeley segmentation dataset.
    Full-Reference [1] / Non-Reference [0] : 1
    The last part of the file can have any description you want about the code producing the provided results (dependencies, link, scripts, etc.)
    The provided information is very important both during the validation period when different teams can compare their results/solutions but also for establishing the final ranking of the teams and their methods.
  3. Create a ZIP archive named submission.zip (must in this name) containing only output.txt and readme.txt and submit this zip file.

New Trends in Image Restoration and Enhancement (NTIRE) workshop and challenges @ CVPR 2021

 

Perceptual Image Quality Assessment Challenge

These are the official rules (terms and conditions) that govern how the NTIRE 2021 challenge on Perceptual Image Quality Assessment will operate. This challenge will be simply referred to as the "challenge" or the "contest" throughout the remaining part of these rules and may be named as "NTIRE" or "PIPAL" benchmark, challenge, or contest, elsewhere (our webpage, our documentation, other publications).

In these rules, "we", "our", and "us" refer to the organizers (Jinjin Gu <jinjin.gu@sydney.edu.au>, Jimmy S. Ren <rensijie@sensetime.com>, Chao Dong <chao.dong@siat.ac.cn>, Shuhang Gu <shuhanggu@gmail.com>, and Radu Timofte ) of NTIRE challenge and "you" and "yourself" refer to an eligible contest participant.

Note that these official rules can change during the contest until the start of the final phase. If at any point during the contest the registered participant considers that can not anymore meet the eligibility criteria or does not agree with the changes in the official terms and conditions then it is the responsibility of the participant to send an email to the organizers such that to be removed from all the records. Once the contest is over no change is possible in the status of the registered participants and their entries.

1. Contest description

This is a skill-based contest and chance plays no part in the determination of the winner (s).

The goal of the contest is to predict the perceptual quality of an input image and the challenge is called Perceptual Image Quality Assessment.

The focus of the contest: it will be made available a dataset adapted for the specific needs of the challenge. The images have a large diversity of contents. We will refer to this dataset, its partition, and related materials as NTIRE Dataset. The dataset is divided into training, validation and testing data. We focus on perceptual quality of the results, the aim is to achieve predictions with the best fidelity/correlation to the reference ground truth images/labels. The participants will not have access to the ground truth images/labels from the test data. The ranking of the participants is according to the performance of their methods on the test data. The participants will provide descriptions of their methods, details on (run)time complexity, platform and (extra) data used for modelling. The winners will be determined according to their entries, the reproducibility of the results and uploaded codes or executables, and the above-mentioned criteria as judged by the organizers.

2. Tentative contest schedule

The registered participants will be notified by email if any changes are made to the schedule. The schedule is available on the NTIRE workshop web page and on the Overview of the Codalab competition.

2. Tentative contest schedule

The registered participants will be notified by email if any changes are made to the schedule. The schedule is available on the NTIRE workshop web page and on the Overview of the Codalab competition.

3. Eligibility

You are eligible to register and compete in this contest only if you meet all the following requirements:

  • you are an individual or a team of people willing to contribute to the open tasks, who accepts to follow the rules of this contest
  • you are not an NTIRE challenge organizer or an employee of NTIRE challenge organizers
  • you are not involved in any part of the administration and execution of this contest
  • you are not a first-degree relative, partner, household member of an employee or of an organizer of NTIRE challenge or of a person involved in any part of the administration and execution of this contest

This contest is void wherever it is prohibited by law.

Entries submitted but not qualified to enter the contest, it is considered voluntary and for any entry, you submit NTIRE reserves the right to evaluate it for scientific purposes, however under no circumstances will such entries qualify for sponsored prizes. If you are an employee, affiliated with or representant of any of the NTIRE challenge sponsors then you are allowed to enter in the contest and get ranked, however, if you will rank among the winners with eligible entries you will receive only a diploma award and none of the sponsored money, products or travel grants.

NOTE: industry and research labs are allowed to submit entries and to compete in both the validation phase and final test phase. However, in order to get officially ranked on the final test leaderboard and to be eligible for awards the reproducibility of the results is a must and, therefore, the participants need to make available and submit their codes or executables. All the top entries will be checked for reproducibility and marked accordingly.

We will have 3 categories of entries in the final test ranking:
1) checked with publicly released codes 
2) checked with publicly released executable
3) unchecked (with or without released codes or executables)

 

4. Entry

In order to be eligible for judging, an entry must meet all the following requirements:

Entry contents: the participants are required to submit image results and code or executables. To be eligible for prizes, the top-ranking participants should publicly release their code or executables under a license of their choice, taken among popular OSI-approved licenses (http://opensource.org/licenses) and make their code or executables online accessible for a period of not less than one year following the end of the challenge (applies only for top three ranked participants of the competition). To enter the final ranking the participants will need to fill out a survey (fact sheet) briefly describing their method. All the participants are also invited (not mandatory) to submit a paper for peer-reviewing and publication at the NTIRE Workshop and Challenges (to be held online on June 2021). To be eligible for prizes, the participants' score must improve the baseline performance provided by the challenge organizers.

Use of data provided: all data provided by NTIRE are freely available to the participants from the website of the challenge under license terms provided with the data. The data are available only for open research and educational purposes, within the scope of the challenge. NTIRE and the organizers make no warranties regarding the database, including but not limited to warranties of non-infringement or fitness for a particular purpose. The copyright of the images remains in the property of their respective owners. By downloading and making use of the data, you accept full responsibility for using the data. You shall defend and indemnify NTIRE and the organizers, including their employees, Trustees, officers and agents, against any and all claims arising from your use of the data. You agree not to redistribute the data without this notice.

  • Test data: The organizers will use the test data for the final evaluation and ranking of the entries. The ground truth test data will not be made available to the participants during the contest.
  • Training and validation data: The organizers will make available to the participants a training dataset with ground truth images and a validation dataset without ground truth images. At the start of the final phase, the test data without ground truth images will be made available to the registered participants.
  • Post-challenge analyses: the organizers may also perform additional post-challenge analyses using extra data, but without effect on the challenge ranking.
  • Submission: the entries will be on-line submitted via the CodaLab web platform. During the development phase, while the validation server is online, the participants will receive immediate feedback on validation data. The final perceptual evaluation will be computed on the test data submissions, the final scores will be released after the challenge is over.
  • Original work, permissions: In addition, by submitting your entry into this contest you confirm that to the best of your knowledge: - your entry is your own original work; and - your entry only includes material that you own, or that you have permission to use.

5. Potential use of entry

Other than what is set forth below, we are not claiming any ownership rights to your entry. However, by submitting your entry, you:

Are granting us an irrevocable, worldwide right and license, in exchange for your opportunity to participate in the contest and potential prize awards, for the duration of the protection of the copyrights to:

  1. Use, review, assess, test and otherwise analyze results submitted or produced by your code or executable and other material submitted by you in connection with this contest and any future research or contests by the organizers; and
  2. Feature your entry and all its content in connection with the promotion of this contest in all media (now known or later developed);

Agree to sign any necessary documentation that may be required for us and our designees to make use of the rights you granted above;

Understand and acknowledge that us and other entrants may have developed or commissioned materials similar or identical to your submission and you waive any claims you may have resulting from any similarities to your entry;

Understand that we cannot control the incoming information you will disclose to our representatives or our co-sponsor’s representatives in the course of entering, or what our representatives will remember about your entry. You also understand that we will not restrict work assignments of representatives or our co-sponsor’s representatives who have had access to your entry. By entering this contest, you agree that use of information in our representatives’ or our co-sponsor’s representatives unaided memories in the development or deployment of our products or services does not create liability for us under this agreement or copyright or trade secret law;

Understand that you will not receive any compensation or credit for use of your entry, other than what is described in these official rules.

If you do not want to grant us these rights to your entry, please do not enter this contest.

6. Submission of entries

The participants will follow the instructions on the CodaLab website to submit entries

The participants will be registered as mutually exclusive teams. Each team is allowed to submit only one single final entry. We are not responsible for entries that we do not receive for any reason, or for entries that we receive but do not work properly.

The participants must follow the instructions and the rules. We will automatically disqualify incomplete or invalid entries.

7. Judging the entries

The board of NTIRE will select a panel of judges to judge the entries; all judges will be forbidden to enter the contest and will be experts in causality, statistics, machine learning, computer vision, or a related field, or experts in challenge organization. A list of the judges will be made available upon request. The judges will review all eligible entries received and select (three) winners for each or for both of the competition tracks based upon the prediction score on test data. The judges will verify that the winners complied with the rules, including that they documented their method by filling out a fact sheet.

The decisions of these judges are final and binding. The distribution of prizes according to the decisions made by the judges will be made within three (3) months after completion of the last round of the contest. If we do not receive a sufficient number of entries meeting the entry requirements, we may, at our discretion based on the above criteria, not award any or all of the contest prizes below. In the event of a tie between any eligible entries, the tie will be broken by giving preference to the earliest submission, using the time stamp of the submission platform.

8. Prizes and Awards

The financial sponsors of this contest are listed on NTIRE 2021 workshop web page . There will be economic incentive prizes and travel grants for the winners (based on availability) to boost contest participation; these prizes will not require participants to enter into an IP agreement with any of the sponsors, to disclose algorithms, or to deliver source code to them. The participants affiliated with the industry sponsors agree to not receive any sponsored money, product or travel grant in the case they will be among the winners.

Incentive Prizes for each track competitions (tentative, the prizes depend on attracted funds from the sponsors)

  • 1st place: ?00$ + ?GPU + ?award certificate
  • 2nd place: ?00$ + ?GPU + ?award certificate
  • 3rd place: ?00$ + ?award certificate

9. Other Sponsored Events

Publishing papers is optional and will not be a condition to entering the challenge or winning prizes. The top-ranking participants are invited to submit a paper following CVPR2021 author rules, for peer-reviewing to NTIRE workshop.

The results of the challenge will be published together with NTIRE 2021 workshop papers in the 2021 CVPR Workshops proceedings.

The top-ranked participants and participants contributing interesting and novel methods to the challenge will be invited to be co-authors of the challenge report paper which will be published in the 2021 CVPR Workshops proceedings. A detailed description of the ranked solution, as well as the reproducibility of the results, are a must to be an eligible co-author.

10. Notifications

If there is any change to data, schedule, instructions of participation, or these rules, the registered participants will be notified on the competition page and/or at the email they provided with the registration.

Within seven days following the determination of winners we will send a notification to the potential winners. If the notification that we send is returned as undeliverable, or you are otherwise unreachable for any reason, we may award the prize to an alternate winner, unless forbidden by applicable law.

The prize such as money, product, or travel grant will be delivered to the registered team leader given that the team is not affiliated with any of the sponsors. It is up to the team to share the prize. If this person becomes unavailable for any reason, the prize will be delivered to be the authorized account holder of the e-mail address used to make the winning entry.

If you are a potential winner, we may require you to sign a declaration of eligibility, use, indemnity and liability/publicity release and applicable tax forms. If you are a potential winner and are a minor in your place of residence, and we require that your parent or legal guardian will be designated as the winner, and we may require that they sign a declaration of eligibility, use, indemnity and liability/publicity release on your behalf. If you, (or your parent/legal guardian if applicable), do not sign and return these required forms within the time period listed on the winner notification message, we may disqualify you (or the designated parent/legal guardian) and select an alternate selected winner.

 


The terms and conditions are inspired by and use verbatim text from the `Terms and conditions' of ChaLearn Looking at People Challenges and of the NTIRE 2017, 2018, 2019 and 2020 challenges and of the AIM 2019 and 2020 challenges .

NTIRE Workshop and Challenges @ CVPR 2021

 

Organizers

 

The NTIRE challenge on Perceptual Image Quality Assessment is organized jointly with the NTIRE 2021 workshop.  The results of the challenge will be published at NTIRE 2021 workshop and in the CVPR 2021 Workshops proceedings.

 

Jinjin Gu <jinjin.gu@sydney.edu.au>, Jimmy S. Ren <rensijie@sensetime.com>, Chao Dong <chao.dong@siat.ac.cn>, Shuhang Gu <shuhanggu@gmail.com>, and Radu Timofte <radu.timofte@vision.ee.ethz.ch> are the contact persons and direct managers of the NTIRE Perceptual Image Quality Assessment challenge.

 

More information about NTIRE workshop and challenge organizers is available here: https://data.vision.ee.ethz.ch/cvl/ntire21/

Development

Start: Jan. 1, 2021, 11:59 p.m.

Testing

Start: March 1, 2021, 11:59 p.m.

Competition Ends

March 8, 2021, 11:59 p.m.

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# Username Score
1 bqy2020 1.56
2 zhangtaotao 1.52
3 zakajd 1.40