Coding and Decoding Schemes for MSE and Image Transmission

TL;DR

This paper investigates coding and decoding strategies optimized for mean squared error in image transmission, introducing a new loss function and exploring unequal error protection techniques with practical examples and visual simulations.

Contribution

It proposes a novel loss function for coding schemes focused on MSE and demonstrates the use of ordered decoders for message-wise UEP in image transmission.

Findings

Explicit examples with gray-scale images show improved error protection.

Visual simulations demonstrate the effectiveness of the proposed schemes.

Performance analysis indicates potential for better image quality in noisy channels.

Abstract

In this work we explore possibilities for coding and decoding tailor-made for mean squared error evaluation of error in contexts such as image transmission. To do so, we introduce a loss function that expresses the overall performance of a coding and decoding scheme for discrete channels and that exchanges the usual goal of minimizing the error probability to that of minimizing the expected loss. In this environment we explore the possibilities of using ordered decoders to create a message-wise unequal error protection (UEP), where the most valuable information is protected by placing in its proximity information words that differ by a small valued error. We give explicit examples, using scale-of-gray images, including small-scale performance analysis and visual simulations for the BSMC.