| Summary: | Background: With the rise of streaming services such as cloud gaming, a fast internet speed is required for the overall experience. The average internet connection is not suited for the requirements that cloud gaming require. A high quality and frame rate is important for the experience. A solution to this problem would be to have parts where the user is looking at in a image be displayed in higher quality compared to the rest of the image. Objectives: The objective of this thesis is to create a gaze-based lossy image compression algorithm that reduces quality where the user is not looking. By using different radial functions to determine the quality decrease, the perceptual quality is compared to traditional JPEG compression. The storage difference when using a gaze-based lossy image compression is also compared to the JPEG algorithm. Methods: A gaze-based image compression algorithm, which is based on the JPEG algorithm, is developed with DirectX 12. The algorithm uses Tobii eye tracker to get where the user is gazing at the screen. When the gaze-position is changed the algorithm is run again to compress the image. A user study is conducted to the test the perceived quality of this algorithm compared to traditional lossy JPEG image compression. Two different radial functions are tested with various parameters to determine which one is offering the best perceived quality. The algorithm is also tested along with the radial functions on how much of a storage difference there is when using this algorithm compared to traditional JPEG compression. Results: With 11 participants, the results show the gaze-based algorithm is perceptually the same on images that have few objects who are close together. Images with many objects that are spread throughout the image performed worse on the gaze-based algorithm and was less picked compared traditional JPEG compression. The radial functions that cover much of the screen is more often picked compared to other radial functions that have less area of the screen. The storage difference between the gaze-based algorithm compared to traditional JPEG compression was between 60% to 80% less depending on the image. Conclusions: The thesis concludes that there is substantial storage savings that can be made when using a gaze-based image compression compared to traditional JPEG compression. Images with few objects who are close together are perceptually not distinguishable when using the gaze-based algorithm.
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