A good image resolution provides excellent picture quality. Resolution is important for photography because it can define details, lines, and edges. Improving resolution is a good thing. In today’s review, we’ll talk about resolution and a few methods of improving it.
By the way, if you need an AI upscaling image enhancement application, use Luminar Neo. With it, you don’t have to understand the intricacies of working with a photo editor, as its advanced artificial intelligence will do most of the work for you. In our opinion, it is one of the best upscaler AI currently available.
What Is Resolution?
To work with any AI upscaler, you first have to understand what you have to work with.
The resolution is the number of dots (raster image elements) per unit area (or unit length). The term is usually applied to images in digital form, although it can be applied, for example, to describe the granulation level of photographic film, photographic paper, or other physical media. Higher resolution (more elements) typically provides more accurate representations of the original. Another important image characteristic is the resolution of the color palette.
As a rule, the resolution in different directions is the same, which gives the pixel a square shape. But this is not necessarily true. For example, the horizontal resolution may be different from the vertical resolution, and the image element (pixel) will not be square, but rectangular. When a picture is displayed on a screen or paper surface, it occupies a rectangle of a certain size. For optimal picture placement, you need to coordinate the number of dots and the aspect ratio of the file with the corresponding parameters of the display device.
If the pixels of the image are rendered by the pixels of the output device one-to-one, the size will be determined only by the resolution of the output device. Accordingly, the higher the resolution of the screen, the more dots are displayed in the same area, and the less grainy and higher quality your picture will be.
- With a large number of dots placed on a small area, the eye does not notice the mosaic nature of the picture.
- The opposite is true: a low resolution will allow the eye to notice the image rasterization (“steps”).
High resolution of an image with a small size of a displaying device plane will not let you see the whole picture or the file will be “adjusted”, for example, each displayed pixel will have the averaged color of the part of the original image in it. If you want to display an image of a small size on a device with a high resolution you have to calculate the colors of intermediate pixels. Changing the actual number of pixels in an image is called oversampling, and there are a number of algorithms of varying complexity for it.
Image upscaler with AI is the simplest of all conversion methods, in which the processor (even of an ordinary television) will itself add extra pixels to match its resolution.
The final image, although it will have the appropriate resolution, will be a lot of new averaged pixels, the terrible quality of which will be reflected in the final total blurring of the image, especially in comparison with other methods of picture and resolution conversion.
Naturally, in the end, comparing the methods of construction, we can say that natural native resolution gives the best picture in the end. At that, of course, there is a dependence on necessary power. The better picture you want and the better resolution method to use, the more powerful system you need.
AI upscale software works in different ways and below we will look at some of them.
Styles are useful when you want to set the same size for lots of images in bulk, then you don’t have to specify individual sizes for each image via width and height. But if you have a lot of differently sized images, then using styles won’t help at all. They are useful, for example, for icons with the same width and height, or when sizes are specified as percentages.
Suppose you have a 200×200 pixel bitmap and want it to be doubled in width. The area of the picture and the total number of pixels grows by a factor of four. New pixels are added by AI image upscaling software independently of the browser based on the set of pixels you already have. The way of getting these new pixels is called image interpolation. It should be understood that the quality depends very much on the file itself, the scale, and the algorithm, but usually, the result is worse than the original.
A similar thing happens when you shrink an image, but the browser does not have to add, but eject some pixels.
The interpolation algorithm is built into the browser and can be changed using the image-rendering property. Unfortunately, browsers do not yet have much support for this property, so you have to specify several different values.