Advances in video technology are being incorporated into today’s healthcare practice. For example, colonoscopy is an important screening tool for colorectal cancer. Colonoscopy allows for the inspection of the entire colon and provides the ability to perform a number of therapeutic operations during a single procedure. During a colonoscopic procedure, a tiny video camera at the tip of the endoscope generates a video signal of the internal mucosa of the colon. The video data are displayed on a monitor for real-time analysis by the endoscopist. Other endoscopic procedures include upper gastrointestinal endoscopy, enteroscopy, bronchoscopy, cystoscopy, and laparoscopy. However, a significant number of out-of-focus frames are included in this type of videos since current endoscopes are equipped with a single, wide-angle lens that cannot be focused. The out-of-focus frames do not hold any useful information. To reduce the burdens of the further processes such as computer-aided image processing or human expert’s examinations, these frames need to be removed. We call an out-of-focus frame as non-informative frame and an in-focus frame as informative frame. We propose a new technique to classify the video frames into two classes, informative and non-informative frames using a combination of Discrete Fourier Transform (DFT), Texture Analysis, and K-Means Clustering. The proposed technique can evaluate the frames without any reference image, and does not need any predefined threshold value. Our experimental studies indicate that it achieves over 96% of four different performance metrics (i.e. precision, sensitivity, specificity, and accuracy).