Detecting aligned double joint photographic experts group (JPEG) compression is a challenging task in digital image forensics. Previous studies have proposed methods that can effectively detect aligned double JPEG compression, but these methods mostly rely on features extracted during the JPEG decompression process. If the aligned double compressed JPEG image is saved in BMP format, these methods may be difficult to be directly applied. To address this issue, this study proposes a quantization step estimation method based on dual thresholds, which allows for the acquisition of quantization tables and the extraction of features. Furthermore, the study defines a minimum error based on the unique properties of JPEG compression with a quality factor of 100, and by removing the minimum error from the features, the feature detection performance of the proposed method is further improved. Finally, the study extracts first-order relative error features based on the convergence properties of the de-quantized JPEG coefficients, which further enhances the detection performance of the proposed method at lower quality factors. Experimental results demonstrate that the proposed method outperforms current state-of-the-art algorithms at different quality factors.