Reducing Image Size Without Perceivable Quality Loss: Resampling Principles and Operation Guide

From the perspective of information theory, the total amount of pixel information contained in an original high-definition image is far greater than the amount of information required for the reduced target size. The reduction process only discards redundant repeated information and does not alter the texture structure of core regions.

The human eye has an upper limit for resolving image details. Take a 1080P display as an example: when the width of a single image exceeds 1920 pixels, the extra pixels cannot be perceived by the human eye, so reducing the image to a matching size will not cause visible quality degradation.

The essence of image upscaling is to infer new pixels based on existing pixels. Details that were not recorded during the original shooting process cannot be generated through interpolation, and no algorithm can create non-existent image information out of thin air.

AI super-resolution algorithms can use texture features learned from training datasets to reasonably complete upscaled regions and improve visual perception, but they cannot restore real details beyond what the original image recorded, and the completion results have probabilistic deviations.

The core operation of image scaling is resampling, which calculates the pixel value of target positions based on original pixels. Different interpolation algorithms have different calculation logics, resulting in differences in output effects. The OKfmt image resizer supports three commonly used algorithms: Lanczos, Bicubic, and Bilinear.

The core differences between the three algorithms lie in the sampling area and weight calculation method. We have compiled a comparison of applicable scenarios for reference:

The aspect ratio of an original image is determined by shooting parameters. Manually modifying one dimension and then adjusting the other will forcibly stretch pixels, causing deformation of objects in the frame, such as widened faces and tilted buildings.

Mainstream image processing tools lock aspect ratio by default: after entering the width, the height is automatically calculated according to the original proportion, and entering the height automatically calculates the width. This avoids frame deformation and ensures the original proportion of image content.

Apple Retina displays and most Android HD displays use 2x pixel rendering, where 1 logical pixel corresponds to 2×2 physical pixels. Outputting an image at logical size will result in blurriness on HD displays.

When calculating the 2x output size, simply multiply the required logical size specified in the design by 2. For example, if the web design requires a display size of 300 logical pixels in width, the output image width should be 600 pixels to meet the display requirements of Retina screens.