The wavelet block tree coding (WBTC) algorithm is an efficient wavelet‐based image coder. In this coder, multiple spatial orientation trees are combined together to make a single block tree. It uses three ordered lists to keep track of significant/insignificant coefficients and sets while coding, which increases its memory requirement as well as computational complexity. Also, it uses memory inefficient conventional discrete wavelet transform (DWT) to compute the transformed coefficients. In this study, a Low‐Complexity Block Tree Coding (LCBTC) algorithm that uses two state‐tables and two very small lists, is proposed. Similar to WBTC, it also uses sorting and refinement passes in each bit‐plane. However, it encodes the coefficients in block‐tree manner using depth‐first search approach to reduce the computational complexity. It uses DWT coefficients obtained from modified fractional wavelet filter (MFrWF) rather than conventional DWT, which further reduces the overall memory and complexity of the image coder. The simulation results show that the memory requirement and computational complexity of LCBTC is much less than WBTC and other state‐of‐the‐art coding algorithms. These features make the image coder a better candidate for compression in memory constrained and real‐time visual sensor networks.