Traditionally, image compression algorithms have primarily focused on optimizing storage without considering resource-constrained applications, such as wireless sensor networks (WSNs). However, for practical application in WSNs, a balanced trade-off between compression ratio, distortion, and energy consumption is crucial to improve the network lifetime while maintaining acceptable image reconstruction quality at lower bit and error rates. Previous studies have focused on higher image compression rates to address storage limitations rather than on the optimization of WSNs. In addition, most previous research on image compression for WSNs either requires an error-bound mechanism or compromises the trade-off between compression ratios and reconstructed image quality measured using image quality assessment metrics, such as root mean square error (RMSE), and coefficient of determination ( $R^{2}$ ), leading to a reduced network lifetime and uncontrolled reconstructed image quality that is not application-specific. Therefore, we present an optimized block-based image compression algorithm for WSNs with a relative error-bound mechanism that adapts to a given dataset to improve reconstruction fidelity and energy consumption at higher compression ratios. A comparison of our proposed algorithm with existing algorithms demonstrated that using a convolutional variational autoencoder and relative error-bound mechanism leads to a significant trade-off in distortion, compression ratio, and energy consumption in WSNs. Our results demonstrated that an average reconstruction fidelity of more than 90% was achieved using image quality evaluation metrics at compression ratios of 60% or more. Furthermore, more than 50% energy conservation at compression ratios greater than 60% from image compression was achieved compared with the transmission of raw data within a WSN.