What is Image Resizing? A Computer Vision Guide.
Blog post from Roboflow
Image resizing, also known as scaling or resampling, is a crucial aspect of image processing and computer vision, involving the alteration of a digital image's dimensions through upscaling or downscaling. This process changes the number of pixels in an image, affecting its visual size and the data required for representation, and is performed using interpolation techniques to maintain visual quality. The OpenCV Python package offers the cv2.resize() function for resizing images, allowing specification of new dimensions or scaling factors and various interpolation methods like nearest neighbor, bilinear, bicubic, Lanczos, and area interpolation to enhance resizing quality. Resizing images is essential in computer vision for maintaining consistency in dataset dimensions, improving model performance by ensuring all images conform to fixed sizes required by machine learning models, and optimizing computational efficiency. Techniques such as maintaining or altering aspect ratios, padding, or cropping are applied to prepare images for model training, with methods like "Stretch to" and "Fit within" addressing different resizing needs. Proper resizing is vital for effective model training, preventing distortion, and ensuring image quality and detail are preserved.