In the last two decades, there exist many high-performance prediction-based methods that use different coefficients of causal neighbors in order to exploit the relationship of spatial energy to produce a less error image. Besides, more and more researches focus on the accuracy of predictor; nevertheless, the predictor spends a lot of time on finding the best coefficients of causal neighbors. The objective of our research is to propose an efficient and implementable method to improve compression ratio, without increasing extra computation complexity. Here, we present an improved lossless image compression based on the prediction method, by the proposed application of efficient error value centralization by sign bits. The contribution of this paper is to centralize error values in a novel way to improves coding performance. Experimental results show that our proposed method achieves higher compression ratio than the context-based, adaptive, and lossless image codec (CALIC) method for the images with many details or slightly regular texture.