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OSLO: On-the-Sphere Learning for Omnidirectional images and its application to 360-degree image compression
State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs).
Fine granularity access in interactive compression of 360-degree images based on rate-adaptive channel codes
This requires two characteristics: efficient compression of data, to lower the storage cost, and random access ability to extract part of the compressed stream requested by the user (for reducing the transmission rate).
A location-aware embedding technique for accurate landmark recognition
The current state of the research in landmark recognition highlights the good accuracy which can be achieved by embedding techniques, such as Fisher vector and VLAD.
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