Image size is directly related to image resolution i.e. the higher the resolution, the bigger the file size

The Relationship of Image Resolution To Image Size

An image captured on a digital CMOS sensor camera is saved to a storage media (SD/SDXC card) in a file format. For best results typically shoot in RAW, a lossless uncompressed file format which preserves all the details captured. It is also the best format to use for making adjustments to an image before final post processing, yet not all images captured are the exact same file size. The file size is not the same as resolution, though resolution does help determine quality and overall file size.

File size is measured in bytes, typically with today’s cameras images captured are stored in files measured in MB (Megabytes). This is due to the many differences in detail stored in an image that is determined by lighting, exposure and shutter speed. Some images will have more detail than others depending on your camera settings. Even if the images shot in RAW are all the same resolution, they will still not have the same file size across all the images captured, but will be close. There is another thing that determines the file size and it is called the bit depth.

Calculating Image Size

Images are made of a grid of pixels aka “picture elements”. A pixel contains 8 bits (1 byte) if it is in BW (black and white). For colored images it uses a certain color scheme called RGB (Red, Green, Blue) represented as 1 byte each or 24 bits (3 bytes) per pixel. This is also referred to as the bit depth of an image. To determine bit depth you need the number of bits used to define each pixel.

The > the bit depth, the > the number of tones (grayscale or color) that can be represented. 

Digital images may be produced in black and white (bitonal), grayscale or color. Each color has a varying level determined by exponential values from 256 colors for 8 bit and 16,777,216 colors for 24 bit images. So a bit depth of 24 bits represents 16.7 million tonal representation of color. Image resolution is just the size of the images width(W) and height(H) measured in number of pixels.

To get the size of the image the resolution will be needed.

(W x H x BitDepth) / 8 bits/byte = (W x H x BitDepth) x 1 byte/8bits

As an example let’s say the image has the following dimensions:

W = 4928 pixels
H = 3264 pixels
BitDepth = 24 bits/pixel

= (4928 x 3264) x 24 bits/pixel / 8 bits/byte
= 16,084,992 pixels x 24 bits/pixel / 8 bits/byte
= 386,039,808 bits / 8 bits/byte
= 386,039,808 bits x 1 byte / 8 bits
= 48,254,976 bytes
= 48 MB

As an estimate, a 4 GB SD card can store about 89 images at 48 MB each. At 32 GB up to 712 images can be stored. This will once again vary depending on what a photographer is shooting (wedding, sports, fashion, events) and the detail that is captured in color or black and white.

Note: The image size is an approximation based on the dimensions. They can vary from image to image depending on the details they contain in color, depth and lightness. For example, even if the bit depth is 24 bits, not all those bits will show a uniform tone or color, but rather show gradients of the RGB color spectrum’s gamut.

When planning for shoots with a DSLR or other digital camera, make sure you have enough capacity on your SD card. For commercial and wedding photographers who need to shoot non-stop, a fast write operation and high capacity storage device is ideal. (Photo Source Panasonic)

Reasons To Shoot In High Resolution

One reason to shoot in high resolution is the image can be blown up to a large print format. This is ideal for publishing and advertising, where the maximum resolution determines the final quality of the output image. For example, if you want to print an image the size of a billboard, the best result is from an uncompressed high resolution image because the magnification scale is at a more acceptable level. High resolution images, when magnified or up scaled from its original size still appear sharp and detailed. Whereas a lower resolution image when blown up becomes blurry, pixelated (noticeable pixelations) and does not retain sharpness in detail.

(L) 4928 pixels (horizontal) at 300% magnification. (R) 1200 pixels (horizontal) at 300% magnification. At higher resolutions, you can get better details when zooming in. At lower resolutions, the image gets more blurry and loses plenty of details when zooming in. This is even worse when the low resolution image uses a lossy compression algorithm.

(L) 4928 pixels (horizontal) at 1300% magnification. (R) 1200 pixels (horizontal) at 1300% magnification. When further magnifying or up scaling a lower resolution image, the “staircase effect” or jagged edges start to appear and the image becomes less clear, more blurry. This is the result of aliasing, and photo editing software use a technique called anti-aliasing to minimize this. The higher resolution image on the other hand, appears more detailed even at higher zoom. Although the pixels become noticeable at close distance to the eye, it does not matter from a large distance. For example, when viewing a billboard display, the users will not notice the pixels that much but will instead see the larger scaled image without the aliasing and blurry effect. Commercial and advertisements shoot in high resolution for the purpose of blowing the image up or scaling it for large prints.

The Uses For Low Resolution

Uploading images to the web for content creation is much different. Web resolution does not have to be so high, and priority is sometimes on smaller image size for faster download time. In this case compressed, lower resolution formats are actually acceptable since they don’t have to be viewed in a large print format. Instead the output is a typical screen display, which are mostly at least 720 or 1080 pixel resolution on the vertical.

In some image editing programs, lossy compression is used to reduce the file size for uploading to the web. This technique, while great at reducing the file’s size, suffers from generation loss. This means the image through a repeated process of compression will loss the details of the original image’s quality. This is typical with JPEG image formats. This uses an algorithm technique called Discrete Cosine Transforms (DCT) that saves the image using some form of lossy compression.

For web content (e.g. lookbooks, e-commerce catalogues, portfolios, etc.) the image resolution does not have to be so high. For one thing, it won’t fit on screen if your display is only 1080 pixels wide, while the image is over 4000 pixels wide. Instead the images are resized to appear on the website to fit. Users can still download the original image in its original resolution.

Summary

The > image size, the more pixels an image contains, the higher the resolution, the > space you need for storage. This is in it’s pre-processed or pre-edited format. After editing and saving the file to a compressed format, the file size decreases due to lost details from file compression. The best way to keep that uncompressed detail in the image is to save the file in a lossless format like TIFF rather than JPEG, but the file size will be bigger as well.