Centric Management of Resources in Modern Distributed Computing
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-3315-0_29
Reference14 articles.
1. Hooda R, Pan WD (2022) Early termination of dyadic region-adaptive hierarchical transform for efficient attribute compression of 3D point clouds. IEEE Signal Process Lett 29:214–218
2. Wang T, Li F, Cosman PC (2022) Learning-based rate control for video-based point cloud compression. IEEE Trans Image Process 31:2175–2189
3. Nguyen NHT, Perry S, Bone D, Le Thanh H, Xu M, Nguyen TT (2022) Combination of images and point clouds in a generative adversarial network for upsampling crack point clouds. IEEE Access 10:67198–67209
4. Sheng X, Li L, Liu D, Xiong Z, Li Z, Wu F (2022) Deep-PCAC: an end-to-end deep lossy compression framework for point cloud attributes. IEEE Trans Multimedia 24:2617–2632
5. Yang S, Zhang Z, Zhao C, Song X, Guo S, Li H (2022) CNNPC: end-edge-cloud collaborative cnn inference with joint model partition and compression. IEEE Trans Parallel Distribut Syst 33(12):4039–4056
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