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22 April 2020 Performance analysis of semantic segmentation algorithms trained with JPEG compressed datasets
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Semantic Segmentation using convolutional neural networks is a trending technique in scene understanding. As these techniques are data-intensive, several devices struggle to store and process even a small batch of images at a time. Also, as the volume of training datasets required by the training algorithms is very high, it might be wise to store these datasets in their compressed form. Not only this, in order to correspond the limited bandwidth of the transmission network the images could be compressed before sending to the destination. Joint Photography Expert Group (JPEG) is a famous technique for image compression. However, JPEG introduces several unwanted artifacts in the images after compression. In this paper, we explore the effect of JPEG compression on the performance of several deep-learning-based semantic segmentation techniques for both the synthetic and real-world dataset at various compression levels. For some established architectures trained with compressed synthetic and real-world dataset, we noticed the equivalent (and sometimes better) performances compared to uncompressed dataset with substantial amount of storage space reduced. We also analyze the effect of combining original dataset with the compressed dataset with different JPEG quality levels and witnessed a performance improvement over the baseline. Our evaluation and analysis indicates that the segmentation network trained on compressed dataset could be a better option in terms of performance. We also illustrate that the JPEG compression acts as a data augmentation technique improving the performance of semantic segmentation algorithms.
Conference Presentation
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Suvash Sharma, Christopher Hudson, Daniel Carruth, Matt Doude, John E. Ball, Bo Tang, Chris Goodin, and Lalitha Dabbiru "Performance analysis of semantic segmentation algorithms trained with JPEG compressed datasets", Proc. SPIE 11401, Real-Time Image Processing and Deep Learning 2020, 1140104 (22 April 2020);


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