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In recent years, the rapid advancement of the Internet and the growing number of people using social networking services (SNSs) have facilitated the sharing of multimedia data. However, multimedia data processing techniques such as transcoding and transmoding impose a considerable burden on the computing infrastructure as the amount of data increases. Therefore, we propose a MapReduce-based image-conversion module in cloud computing environment in order to reduce the burden of computing power. The proposed module consists of two parts: a storage system, i.e., Hadoop distributed file system (HDFS) for image data and a MapReduce program with a Java Advanced Imaging (JAI) library for image transcoding. It can process image data in distributed and parallel cloud computing environments, thereby minimizing the computing infrastructure overhead. In this paper, we describe the implementation of the proposed module using Hadoop and JAI. In addition, we evaluate the proposed module in terms of processing time under varying experimental conditions.