October 1, 2026 — 2:00 p.m. Eastern Time (ET)
Here you will find all of the information (appropriate links (including Dropbox folder links for homework), notes, reminders, etc.) for Anthony’s Photoshop 101 (Photoshop Basics for Artists). Any questions, comments, or concerns should be sent to aaawpsclass@gmail.com.
NOTE: Please respect the work, rights, and privacy of participating artists. You may view the uploaded homework efforts from the class within the Dropbox folder, but you may not download or manipulate their work in any way. Anya and I will download uploaded homework or classwork images when needed/appropriate, but we will never share anyone’s images outside the class without the author’s express permission. We will delete all files in the Dropbox folders at the end of the course. In addition, we will not record classes to respect each participant’s learning experience.
If there are files required for the week’s homework, then they will be available in a folder called “WeekX_Resources” in the appropriate week’s folder. You will need to download the files in this folder to complete the week’s homework. However, please be sure not to remove or add anything to this folder.
DROPBOX LINK: Dropbox
WEEK ONE:
Today we cover select fundamental concepts about digital images and related Photoshop image management. This week’s resources folder includes one large example image file for personal homework if you don’t have a large file of your own. This will be addressed in class.
DROPBOX FOLDER LINK:
Again, today we discuss several fundamental aspects of digital imaging. We cover the pixel, the dot, ppi, dpi, resolution, image size, image quality and compression, file size, megapixel, megabyte, and file formats (psd, tiff, jpeg, png, gif, raw, cr2)
What Is a Digital Image Made Of?
A raster image, such as a digital photograph, is organized as a rectangular grid of individual picture elements called pixels. Each pixel occupies a position in that grid and has a color or tonal value.
When the pixels are small enough relative to our viewing distance, we generally see a continuous image rather than individual squares. Enlarging the image makes its pixel structure easier to see. In the illustration, the enlarged smiley face reveals the grid that makes up the smaller image.
What Information Does a Pixel Contain?
For an ordinary RGB image, each pixel’s color is described using three numerical values:
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R — Red
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G — Green
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B — Blue
These three components are called channels. Each channel supplies one part of the information used to reproduce that pixel’s color.
Think of them as three separate controls for red, green, and blue light. When all three channels are at their maximum, the result is white; when all three are zero, the result is black. Equal intermediate settings produce grays. This is different from mixing paints.
Reading the Illustration
The illustration shows the RGB values of three selected pixels as percentages:
| Selected pixel | Red | Green | Blue | Result |
|---|---|---|---|---|
| First | 93% | 93% | 93% | Light gray |
| Second | 35% | 35% | 16% | Dark, muted yellow |
| Third | 90% | 90% | 0% | Bright yellow |
Equal red, green, and blue values produce a neutral gray—or black or white at the endpoints. Different channel values produce other colors. For example, high red and green values combined with little or no blue produce yellow.
The percentages do not need to add up to 100%. Each channel has its own value. They describe three independent settings, not portions of a single mixture.
Where Do Bits Come In?
The computer must store those numerical values. It does so using bits.
A bit is one binary digit: it can be either 0 or 1. One bit therefore has two possible values.
The illustration below shows how this can describe a bitonal image—an image with two values. Each grid cell represents one pixel: 0 means black, and 1 means white.
Only one bit per pixel is needed because a single binary digit tells us which of the two values to use. Think of one light switch with two positions: one switch, two possibilities. The numbers and grid lines are explanatory labels; the image consists of black-and-white pixels.
Combining bits gives us more possibilities:
| Number of bits | Possible codes | Number of possibilities |
|---|---|---|
| 1 | 0, 1 | 2 |
| 2 | 00, 01, 10, 11 | 4 |
| 3 | 000, 001, 010, 011, 100, 101, 110, 111 | 8 |
Each additional bit doubles the number of possible combinations. With eight bits, there are 256 possible combinations. These can represent the whole numbers 0 through 255.
There are 256 values because zero counts as one of the possibilities.
How Do Bits Describe a Pixel’s Color?
The black-and-white example needed only one bit for each pixel because there were only two possible tones. Now return to the RGB example, where each pixel has three channel values. In an 8-bit-per-channel RGB image, eight bits describe the red value, eight describe the green value, and eight describe the blue value:
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Red can have a value from 0 to 255.
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Green can have a value from 0 to 255.
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Blue can have a value from 0 to 255.
The smiley-face illustration expresses RGB channel values as percentages. In an 8-bit-per-channel image, those settings can instead be expressed as whole numbers from 0 to 255, where 0 is the minimum, and 255 is the maximum.
For example:
| Color | Red | Green | Blue |
|---|---|---|---|
| Black | 0 | 0 | 0 |
| White | 255 | 255 | 255 |
| Middle gray | 128 | 128 | 128 |
| Red | 255 | 0 | 0 |
| Yellow | 255 | 255 | 0 |
These numbers are encoded channel settings. They are not direct measurements of perceived brightness.
Why Is This Also Called 24-Bit Color?
Each pixel uses eight bits for each of its three color channels:
8 bits for red + 8 bits for green + 8 bits for blue = 24 bits per pixel.
Each channel has 256 possibilities, so the three channels together allow:
256 × 256 × 256 = 16,777,216 possible RGB combinations.
That is the number of combinations the encoding allows—not the number of colors necessarily present in a particular picture or visibly distinguishable from one another.
The headings in this illustration refer to bits per channel. Each RGB channel has the stated number of possible values; multiplying the three channel counts gives the number of possible RGB combinations.
This illustration compares an image with approximately 16.7 million colors with versions restricted to 256- and 16-color palettes. The visible dot patterns illustrate dithering, which uses arrangements of available colors to approximate additional tones. “256 colors” here means 256 colors for the image’s palette—not 256 values in each RGB channel.
Most color images from digital cameras have 8 bits per channel, meaning each primary color channel (red, green, and blue) can represent 2^8, or 256, different intensity values. When all three channels are combined at each pixel, the total possible colors are (2^8)³ = 16,777,216—often referred to as “true color.” This is why an 8-bit-per-channel RGB image is also described as 24 bits per pixel (8 bits × 3 channels).
What Is a Byte?
A byte is a group of eight bits. The three color values of an 8-bit-per-channel RGB pixel therefore require three bytes of uncompressed color data.
This gives us two different questions to ask about an image:
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How many pixels are there? Its width and height in pixels tell us the size of its grid.
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How much information describes each pixel? Its channels and bit depth tell us how those pixel values are represented.
More pixels can provide a finer grid for recording spatial detail. More bits per channel provide finer numerical steps for describing color and tone, which can help preserve smooth gradations during editing. Increasing one does not automatically increase the other.
For the same pixel dimensions and number of channels, higher bit depth requires more uncompressed image data. Layers, transparency, metadata, and compression also affect the saved file’s size. More on this later!
Vector graphics are computer images created using a sequence of commands or mathematical statements that place lines and shapes in a two-dimensional or three-dimensional space. In vector graphics, a graphic artist’s work, or file, is created and saved as a sequence of vector statements.
Here are a number of common file formats:
Quick format guide for artists:
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PSD: Working master, supports layers/masks.
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TIFF: Commonly used for print delivery and preservation. Choose no compression, LZW, or ZIP to retain image data without lossy compression. TIFF can also use lossy JPEG compression, so the settings matter.
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JPEG: Delivery (small, lossy).
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PNG: Uses lossless compression and supports transparency. Can preserve photographs as well as graphics, but does not retain editable Photoshop layers.
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RAW/CR2: Camera sensor data, processed before editing.
ADDITIONS: Smartphone outputs:
Smartphone users may encounter files ending in .HEIC or .HEIF. HEIF (High Efficiency Image File Format) is a container format for images and related information. HEIC files commonly contain images compressed using HEVC (High Efficiency Video Coding).
These files commonly use lossy compression, although lossless coding is also supported. If a program or upload service does not accept them, create a JPEG copy for broad compatibility, or a PNG or TIFF copy with lossless settings when appropriate.
Converting an already lossy image to a lossless format does not restore discarded information. Keep the original file.
Understanding the Different Meanings of “Size” in our Context Here
When working in Photoshop, “size” can mean several different things. Image dimensions, when expressed in pixels, describe the image’s width and height—for example, 3000 × 2400 pixels. Document size describes the physical dimensions those pixels correspond to at a specified resolution: at 300 pixels per inch (PPI), that image measures 10 × 8 inches. Print size is the actual size of the printed image; it matches the document size when printed without additional scaling, but settings such as “Fit to Page” can change it. Original size needs context: it might mean the physical dimensions of the original artwork or the pixel dimensions of the source photograph before editing. These are not necessarily related—a photograph of a 30 × 24-inch painting could still print at 10 × 8 inches. Finally, file size is the amount of storage the saved file occupies, usually measured in kilobytes (KB) or megabytes (MB). It depends on pixel dimensions, bit depth, layers, file format, and compression. A larger file in megabytes does not automatically mean a larger print or a better-quality image.
So let’s look at how compression affects a file’s size and quality. Here’s a useful infographic that communicates the basic idea of encoding and compression:
Encoding vs. Compression
Image Encoding
Image encoding converts an image into a digital format that can be stored, managed, and transmitted. It defines how the image data (e.g., pixel information, color values) is represented in a digital file. Encoding ensures that different software and hardware can interpret the image correctly. Think of it as the blueprint or structure for organizing the image’s data.
Image Compression:
Image compression is a type of encoding that reduces an image’s file size. This reduction is achieved by removing redundant or less important information from the image data or by representing it more efficiently. The goal of compression is to minimize storage space and transmission bandwidth while maintaining acceptable visual quality.
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Raw pixel data → just the binary grid.
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Run-Length Encoding → repeated values expressed as counts.
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Further Encoding → nesting the RLE into a more compact form.
This is indeed an encoding technique that achieves compression—but the mechanism is not compression in the abstract; it’s the encoding scheme (RLE) that makes compression possible. Remember:
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Encoding = method
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Compression = purpose
Here is a walkthrough of the two basic types of compression (lossless and lossy):
Lossless compression
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The idea: Represents image data more efficiently while allowing the original pixel values to be recovered exactly.
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The visual: A sequence of identical white pixels can sometimes be represented as a count and a color—for example, “five white pixels.” This illustrates one method called run-length encoding.
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The effect: No image information is lost through the compression itself. The storage savings depend on the image and the compression method.
Graphic representation
Original Image (e.g., solid red square)
Data: R, R, R, R, R, R, R, R, R, R
File Size: Large
Lossless Compression
Encoded Data: 10(R)
File Size: Smaller
Decompressed Image
Result: R, R, R, R, R, R, R, R, R, R
Visual Quality: Identical to original
Lossy compression
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The idea: Discards some image information to reduce file size, generally aiming to make the losses less noticeable.
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The visual: Imagine subtle differences among shades of blue being represented with less precision. This is a simplified analogy, not the exact process JPEG uses.
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The effect: Files can become substantially smaller, but discarded information cannot be recovered from that file alone. The changes may be subtle or clearly visible, depending on the image and compression level.
Graphic representation
Original Image (e.g., detailed photo of a sky)
Data: Blue1, Blue2, Blue3, Blue4, Blue5...
File Size: Large
Lossy Compression
Encoded Data: 5(Average Blue)
File Size: Much Smaller
Decompressed Image
Result: Average Blue, Average Blue, Average Blue...
Visual Quality: Degraded, but how noticeable depends on the level of compression.
MP (Megapixels)
A megapixel is one million pixels. To calculate an image’s megapixel count, multiply its width in pixels by its height in pixels, then divide by one million.
For example: 3000 × 2400 pixels = 7,200,000 pixels = 7.2 megapixels.
Megapixels describe a pixel count. They do not specify a physical print size or a file size in megabytes. The number of distinct colors a pixel can represent depends on the number of bits per pixel (bpp). A 1 bpp image uses 1 bit for each pixel, so each pixel can be either on or off. Each additional bit doubles the number of colors available, so a 2 bpp image can have 4 colors, and a 3 bpp image can have 8 colors:
- 1 bpp = 2 colors (black/white-on/off-binary)
- 2 bpp = 4 colors
- 3 bpp = 8 colors
- 4 bpp = 16 colors
- 8 bpp = 256 colors
- 16 bpp = 65,536 colors (“Highcolor” )
- 24 bpp = 16,777,216 colors (“Truecolor”)
Note: In Photoshop and many imaging apps, you’ll also see “16-bit/channel” or “32-bit/channel” modes. This refers to per-channel depth (e.g., 16-bit/channel RGB = 48 bits per pixel total), which allows for far more tonal precision than the simple 1 bpp → 24 bpp ladder.
But how can something like 3BPP yield 8 colors? Like this:
(In the following example, each pixel uses three bits total: one for red, one for green, and one for blue. Each channel can therefore be either off (0) or on (1), producing eight possible combinations.)
| Color | Code | RGB |
|---|---|---|
| Black | O | 000 |
| Blue | B | 001 |
| Green | G | 010 |
| Cyan | C | 011 |
| Red | R | 100 |
| Magenta | M | 101 |
| Yellow | Y | 110 |
| White | 1 | 111 |
Pixel Dimensions and Resolution
Pixel dimensions describe an image’s width and height in pixels. A 1024 × 768-pixel image contains 786,432 pixels, or approximately 0.79 megapixels.
People sometimes use “resolution” to mean pixel dimensions. In Photoshop’s Image Size dialog, however, the Resolution field describes pixel density, commonly measured in pixels per inch (PPI).
PPI — Pixels Per Inch
PPI describes the number of image pixels per linear inch at a particular physical size. For printing, it connects pixel dimensions to dimensions on paper.
Print dimension in inches = pixel dimension ÷ PPI
| Pixel dimensions | PPI at print size | Print dimensions |
|---|---|---|
| 3000 × 2400 pixels | 300 | 10 × 8 inches |
| 3000 × 2400 pixels | 150 | 20 × 16 inches |
Both examples contain the same pixels. The larger print spreads those pixels over a larger area.
DPI — Dots Per Inch
DPI describes a printer’s dot placement along a linear inch. A printer often uses multiple dots to reproduce the color and tone associated with image pixels.
A printer’s advertised 2400 DPI therefore does not mean you need to supply a 2400-PPI image. Increasing printer DPI also does not inherently make the picture smaller.
PPI describes image pixels per inch; DPI describes printer dots per inch. Follow the printer or print provider’s image-preparation requirements.
What About 72 PPI and 300 PPI?
72 PPI is not a required web-image setting. For typical web use, concentrate on the requested pixel dimensions and file size. Changing only an image’s PPI metadata from 72 to 300 does not add pixels or increase its available detail.
Around 300 PPI at the final print size is a common guideline for photographic prints viewed up close. Requirements depend on the printing process, viewing distance, image content, and intended use.
Always connect a PPI requirement to a physical print size. “300 PPI” alone does not tell us how many pixels an image contains. Keep in mind how many of these concepts are tied together.
Estimating Uncompressed Image Data
For a simple raster image:
Uncompressed pixel data in bits = rows × columns × bits per pixel
Consider a 1024 × 1024-pixel, 8-bit grayscale image. Each pixel uses eight bits to represent one of 256 possible gray values.
1024 × 1024 × 8 = 8,388,608 bits
There are eight bits in one byte:
8,388,608 ÷ 8 = 1,048,576 bytes
That is approximately 1.05 MB, or exactly 1 MiB.
MB means megabytes; Mb means megabits. A MiB, or mebibyte, is a binary unit equal to 1,048,576 bytes.
File Size
File size is the amount of storage occupied by the saved file.
The calculation above describes uncompressed pixel data. Compression can reduce storage requirements, while layers, extra channels, previews, and metadata can increase them.
Megapixels count pixels; megabytes measure data storage.
HOMEWORK: Due in Dropbox by October 6th. If you are not well-versed in using Dropbox, you may choose to submit your homework via email at: aaawpsclass@gmail.com.
Scenario (please read carefully!!!): A popular gallery contacts you for a “high-res JPEG” of one of your artworks for use in an upcoming feature in a magazine. They’d also like to add the image to their website. Their IT person is pretty busy right now and asked if you could make a second image ready for website upload. They request “a smaller version with a height ranging anywhere from 700 to 1000px.”
Please put the two files you would send to the gallery in this week’s Dropbox folder or email them to aaawpsclass@gmail.com. Each “correct” file submitted in time will be worth 1 point. Also be sure to use the following filename format for your submitted files:
Filename Format: First Name-Last Name_Title_size_Medium_LARGE/SMALL or PPI
For example: Anthony-Waichulis_Ideation_24x24inches_Oil_LARGE
DROPBOX LINK: Dropbox













