Video summarization is the process of generating a condensed version of the original lengthy video. The growing nature of video capturing, generation and sharing paved a way for the video summarization to be undeniable area of research. It has various applications, including efficient storage, indexing and retrieval. One of the challenges of video summarization is that it is computationally expensive. To overcome this, a simple and effective solution, namely static video summarization using histogram comparison (SVS-HC), is proposed in this research work. Instead of computing features for the whole frame, region of interest is computed by generating spatial saliency map. Using this spatial saliency map, keyframes are extracted. The experimental results are conducted on two benchmark datasets, namely video summarization (VSUMM) and open video project (OVP). The observations reveal that the proposed SVS-HC method outperforms the works of the existing literatures with an average F-score of 63.68 and 65.82 for VSUMM and OVP datasets, respectively.