The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 21, 2026

Filed:

Jun. 12, 2024
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Sounak Dey, Kolkata, IN;

Chetan Sudhakar Kadway, Kolkata, IN;

Arijit Mukherjee, Kolkata, IN;

Arpan Pal, Kolkata, IN;

Sayan Kahali, Kolkata, IN;

Manan Suri, New Delhi, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04N 19/42 (2014.01); H04N 19/156 (2014.01); H04N 19/17 (2014.01); H04N 19/46 (2014.01); H04N 19/91 (2014.01);
U.S. Cl.
CPC ...
H04N 19/42 (2014.11); H04N 19/156 (2014.11); H04N 19/17 (2014.11); H04N 19/46 (2014.11); H04N 19/91 (2014.11);
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.


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