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Rate the quality of 2500+ images (academic study)

Rate the technical quality of photos using the provided Chrome-based app. This is part of an academic study on the perception of image quality.


You will be using our browser-based app (IQAvi) to choose the quality of 2520 images. Use the slider below each image to choose a quality rating from Bad (1) to Excellent (100). Please review the task instructions ([login to view URL]) carefully before participating in the study.

It is MANDATORY to participate in a qualifier contest for your bid to be accepted ([login to view URL] - $70 prize).

We will be accepting 20+ participants in this project.


- experience with photography, design or visual arts

- normal or corrected-to-normal vision

- confidence in assessing and rating the visual quality of images

- participation in the qualifier contest ([login to view URL] - $70 prize)


- Adjust your screen brightness and contrast to clearly see each image. It is possible to miss some details if your screen brightness is too low relative to the ambient light.

- Use a sufficiently large physical screen size, with a resolution greater than 1800x1000 pixels to participate in the study. This will improve your ability to perform the task. Do not use a smartphone or a tablet.

- Use a fast and stable internet connection (5+ Mbps).


1. It is essential to read the task instructions ([login to view URL]) to clearly understand what technical quality is and how to use the scale provided.

2. Open the links here ([login to view URL]) one by one, from top to bottom, in your Chrome web-browser. Each link, or session, contains a batch of about 50 images on average.

3. Fill in your system information, and enter your profile URL when prompted, which will identify your submission.

4. Click on “start experiment”.

5. Switch your browser to Full-Screen mode (bottom-right button in the IQAvi App), to maximise the visible area for large resolution images.

6. Choose the quality for each image by dragging the quality slider below.

7. When finished with a batch of images, click the submit button to get your completion code.

8. Copy and paste each completion code into the corresponding row of the provided .CSV file.

9. Complete all the sessions (including training) and send the filled .CSV file back to us.


- In the training session ([login to view URL]) you will be presented with 57 images with explicit answers. Use this time to practice and better understand the task at hand.

- Providing good quality annotations requires paying attention to details. Please spend sufficient time per image. It is sometimes necessary to spend up to 10 seconds to explore an image in order to find any defects that affect its technical quality.

- Many of the non-training images are meant to assess your level of attention to the task, and accuracy of your answer. Please carefully considered your choices, while trying to be consistent throughout the experiment.


- Some of the images are presented multiple times. Sometimes they are presented at different resolutions, whereas other times they are simply repeated. It is only natural that the quality level assigned for identical images (content and size) should be similar.

- The same image (identical content) present at different resolutions will often have a different quality. This is due to distortion visibility changing with viewing scale.

- In case any of the sessions are not sufficiently well annotated you will be asked to redo them. This is based on your accuracy and the consistency of your answers.

Let us know:

1. The number of years of professional experience you have as a photographer or designer working with photos.

2. What do you think the most difficult part of selecting the correct technical quality for an image is?

Skills: Photography, Data Entry, Visual Arts, Design, Photo Editing

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About the Employer:
( 52 reviews ) Konstanz, Germany

Project ID: #29948597

Awarded to:


Hello, First of all, I won't hurry to complete the project. I will spend sufficient time (Hence I need 5 days for 2520 images) in each image to find the optimal viewing scale. The most difficult part of annotating, as More

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