Since the introduction of digital media for consumers in the 1990s, the amount of images used in everyday life has grown exponentially. All these images need to be stored and broadcasted from some storage device, which has been made possible with the use of image compression algorithms such as JPG. This thesis focuses on exploring the feasibility of using machine learning together with saliency maps to compress images. Two machine learning models were developed, where one used a technique combining the corresponding saliency map with the image as a preprocessing step. The other used a saliency-driven loss function which resulted in much better overall performance. The saliency-driven loss function outperformed the JPG standard on extreme compression rates, however, JPG had better performance on lower and more qualitative compression rates.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-503877 |
| Date | January 2023 |
| Creators | Fällman, Sebastian |
| Publisher | Uppsala universitet, Institutionen för informationsteknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC IT, 1401-5749 ; 23013 |
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