公开(公告)号：US20240422334A1

公开(公告)日：2024-12-19

申请号：US18740775

申请日：2024-06-12

Applicant: Tata Consultancy Services Limited

Inventor： Sounak DEY ,  Chetan Sudhakar KADWAY ,  Arijit MUKHERJEE ,  Arpan PAL ,  Sayan KAHALI ,  Manan SURI

IPC: H04N19/42 ,  H04N19/156 ,  H04N19/17 ,  H04N19/46 ,  H04N19/91

Abstract: This disclosure relates generally to reducing earth-bound image volume with an efficient lossless compression technique. The embodiment thus provides a method and system for reducing earth-bound image volume based on a Spiking Neural Network (SNN) model. Moreover, the embodiments herein further provide a complete lossless compression framework comprises of a SNN-based Density Estimator (DE) followed by a classical Arithmetic Encoder (AE). The SNN model is used to obtain residual errors which are compressed by AE and thereafter transmitted to the receiving station. While reducing the power consumption during transmission by similar percentages, the system also saves in-situ computation power as it uses SNN based DE compared to its Deep Neural Network (DNN) counterpart. The SNN model has a lower memory footprint compared to a corresponding Arithmetic Neural Network (ANN) model and lower latency, which exactly fit the requirement for on-board computation in small satellite.