Most image processing algorithms optimized by GPU can achieve better performance, but the scheduling strategy between data transmission and kernel execution is still the main bottleneck for further improvement in efficiency. To solve this problem, streams are usually used to overlap data transmission and kernel execution, in order to hide some of the data transmission and kernel execution time. However, due to the characteristics of the CUDA programming model and the limitations of GPU resources at hardware level, operations are still serialized when there are so many operations to be execute, even if numerous streams are created. In this paper, a new data storage structure, named Combined Storage Structure (CSS), is proposed, which improves the performance by merging small data transmissions on the single stream into a large one to reduce the fixed cost and the call gap of the operations of data transmission and kernel execution. Experimental results show that CSS can not only improve the performance of GPU-based image processing algorithms in the case of single stream, but also improve the acceleration performance in the case of multiple streams. CSS has good practicability and scalability, and it is suitable for the image processing operations that contain more operators or a large number of small-scale images. In addition, the proposed method provides a new research idea for GPU acceleration of image processing algorithms.