Binary Image 0 1

A binary image is a digital image whose pixels have 2 distinct colors (usually black or white). It is therefore possible to represent a binary image as a series/array of 0 and 1 depending on the brightness of a pixel, which can facilitate its processing.

Read each pixel and if it is dark, convert it to 0, and if it is clear, convert it to 1 (or invert 1 and 0).

If the picture is not in black and white, it will be converted to grayscale according to the Rec. 601 luma (formula $ Y = 0.2989 R + 0.5870 G + 0.1140 B $ ) then binarized according to the selected threshold (generally 0.5 = 50%)

Some people see it as a form of binary art: the pixel art.

Image binarization has many practical applications, especially in image processing:

— OCR (Optical Character Recognition), characters are often written in black on a white background, conversion to binary simplifies processing.

— Barcode reading: for the same reasons as OCR, barcode or QR code recognition is easier in black and white.

— Image display on LED panels or liquid crystal matrices.

— Edge detection: image processing software identifies and extracts the edges of objects in a binary image more easily.

— Motion detection: the same thing, but for videos, comparing binary images greatly accelerates calculation times for video surveillance.

All web image formats (JPG, PNG, GIF, etc.) are accepted, but it is preferable to use a format that uses lossless compression (PNG, BMP, etc.), because in these cases the data of each pixel color is not altered.

Many icons of 16x16, 32x32, 64x64 size make excellent formats.

Binarization is the action of binarizing (make binary with 2 elements) data.

From a practical point of view, an image with 2 colors (coded on 1 bit) is quick to store, each pixel is either 0 or 1.

There is no standard for 1-bit images, but generally 0 codes for black and 1 for white, but nothing prevents the use of 1 for black and 0 for white.

NB: An 8-bit image codes 0 for black and 255 for white.

The source code for image conversion to 0 and 1 could look like this code:// Pseudo-code
function convertToBinaryImage(imageData, threshold = 128) {
binaryImage = []
for each pixel in imageData {
grayscale = (pixel.R + pixel.G + pixel.B) / 3
if (grayscale >= threshold) binaryValue = 1
else binaryValue = 0
binaryImage []= binaryValue
}
return binaryImage
}