Learning-Based Image Compression for Machines
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914959777071104 |
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| author | Gupta, Kartik Faria, Kimberley Mehta, Vikas |
| author_facet | Gupta, Kartik Faria, Kimberley Mehta, Vikas |
| contents | While learning based compression techniques for images have outperformed traditional methods, they have not been widely adopted in machine learning pipelines. This is largely due to lack of standardization and lack of retention of salient features needed for such tasks. Decompression of images have taken a back seat in recent years while the focus has shifted to an image's utility in performing machine learning based analysis on top of them. Thus the demand for compression pipelines that incorporate such features from images has become ever present. The methods outlined in the report build on the recent work done on learning based image compression techniques to incorporate downstream tasks in them. We propose various methods of finetuning and enhancing different parts of pretrained compression encoding pipeline and present the results of our investigation regarding the performance of vision tasks using compression based pipelines. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_19184 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning-Based Image Compression for Machines Gupta, Kartik Faria, Kimberley Mehta, Vikas Image and Video Processing Computer Vision and Pattern Recognition Machine Learning While learning based compression techniques for images have outperformed traditional methods, they have not been widely adopted in machine learning pipelines. This is largely due to lack of standardization and lack of retention of salient features needed for such tasks. Decompression of images have taken a back seat in recent years while the focus has shifted to an image's utility in performing machine learning based analysis on top of them. Thus the demand for compression pipelines that incorporate such features from images has become ever present. The methods outlined in the report build on the recent work done on learning based image compression techniques to incorporate downstream tasks in them. We propose various methods of finetuning and enhancing different parts of pretrained compression encoding pipeline and present the results of our investigation regarding the performance of vision tasks using compression based pipelines. |
| title | Learning-Based Image Compression for Machines |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2409.19184 |