Publication
LEARNING-BASED IMAGE COMPRESSION USING MULTIPLE AUTOENCODERS
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | pt_PT |
| dc.contributor.advisor | Assunção, Pedro António Amado | |
| dc.contributor.advisor | Faria, Sérgio Manuel Maciel de | |
| dc.contributor.advisor | Távora, Luís Miguel de Oliveira Pegado de Noronha e | |
| dc.contributor.author | António, Rúben Duarte | |
| dc.date.accessioned | 2024-01-09T14:05:05Z | |
| dc.date.available | 2024-01-09T14:05:05Z | |
| dc.date.issued | 2023-07-03 | |
| dc.description.abstract | Advanced video applications in smart environments (e.g., smart cities) bring different challenges associated with increasingly intelligent systems and demanding requirements in emerging fields such as urban surveillance, computer vision in industry, medicine and others. As a consequence, a huge amount of visual data is captured to be analyzed by task-algorithm driven machines. Due to the large amount of data generated, problems may occur at the data management level, and to overcome this problem it is necessary to implement efficient compression methods to reduce the amount of stored resources. This thesis presents the research work on image compression methods using deep learning algorithms analyzing the properties of different algorithms, because recently these have shown good results in image compression. It is also explained the convolutional neural networks and presented a state-of-the-art of autoencoders. Two compression approaches using autoencoders were studied, implemented and tested, namely an object-oriented compression scheme, and algorithms oriented to high resolution images (UHD and 360º images). In the first approach, a video surveillance scenario considering objects such as people, cars, faces, bicycles and motorbikes was regarded, and a compression method using autoencoders was developed with the purpose of the decoded images being delivered for machine vision processing. In this approach the performance was measured analysing the traditional image quality metrics and the accuracy of task driven by machine using decoded images. In the second approach, several high resolution images were considered adapting the method used in the previous approach considering properties of the image, like variance, gradients or PCA of the features, instead of the content that the image represents. Regarding the first approach, in comparison with the Versatile Video Coding (VVC) standard, the proposed approach achieves significantly better coding efficiency, e.g., up to 46.7% BD-rate reduction. The accuracy of the machine vision tasks is also significantly higher when performed over visual objects compressed with the proposed scheme in comparison with the same tasks performed over the same visual objects compressed with the VVC. These results demonstrate that the learningbased approach proposed is a more efficient solution for compression of visual objects than standard encoding. Considering the second approach although it is possible to obtain better results than VVC on the test subsets, the presented approach only presents significant gains considering 360º images. | pt_PT |
| dc.identifier.tid | 203458630 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.8/9210 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Learning-based compression | pt_PT |
| dc.subject | Autoencoders | pt_PT |
| dc.subject | Visual objects | pt_PT |
| dc.subject | Video surveillance | pt_PT |
| dc.subject | UHD images | pt_PT |
| dc.subject | 360º images | pt_PT |
| dc.subject | Convolutional neural networks | pt_PT |
| dc.title | LEARNING-BASED IMAGE COMPRESSION USING MULTIPLE AUTOENCODERS | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Engenharia Electrotécnica | pt_PT |