Performance analysis of lossy and lossless image compression using CNN algorithm over novel Naive Bayes algorithm with improved accuracy

Combining a convolutional neural network with the Naive Bayes algorithm is being pursued by researchers in the hopes of improving the accuracy of lossy and lossless picture compression.Materials and Methods: We used a picture dataset to train a 10-sample convolutional neural network with a 10-sample Naive Bayes algorithm to achieve better compression accuracy.Results: The convolutional neural network algorithm outperforms the Naive Bayes Algorithm by a substantial margin, reaching 94.0% accuracy compared to 73.8%.This significant difference between the two groups was confirmed by a 95% confidence interval, which is lower than 0.05 when using an independent sample T-test and a G power value of 95%.Conclusion: Within the parameters of this study, the outcomes demonstrate that the Convolutional Neural Network algorithm achieves better accuracy than the Naive Bayes Algorithm.