Analysing Grayscale Images Using Hamming Code to Identify the Level of Bit Errors that Resulted from Alternative Noise Levels Added

Abstract

Error Detection has an increased importance over the years, protecting data by encrypting or decrypting the data to preserve the desired method to the correct location without data being stolen. There are many error detection methods using various types of algorithms such as Bose-Chaudhurihocquenghem (BCH) and Hamming Code. Hamming code can further be split into three main types; Standard Hamming Code; Extended Hamming Code, and Extended Hamming Product Code. These three types can use even or odd parity to obtain the desired results, mirroring TV devices and satellite channels in this study, an image had Noise with various levels added to it, measuring the Bit Error Rate before and after correction, with Bit Error Rate resulting in 0 for Noise Levels >= 6. With many different types of errors ranging from single to burst errors, finding an algorithm to implement with Hamming Code to reduce Bit Error after correction without repeating the algorithm is a key target for future Error Detection.