Find the histogram of the 4 ×4 8-bit image and derive its histogram-based features. [ 120

Find the histogram of the 4 ×4 8-bit image and derive its...

Find the histogram of the $4 \times 4$ 8-bit image and derive its histogram-based features.
$$
\left[\begin{array}{rrrr}
120 & 103 & 83 & 78 \\
97 & 99 & 81 & 72 \\
102 & 96 & 78 & 73 \\
121 & 107 & 37 & 0
\end{array}\right]
$$

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Key Concepts

8-bit Image Representation

An 8-bit image uses 8 bits to represent each pixel, which allows for 256 distinct intensity levels ranging from 0 to 255. This is the standard for grayscale images and serves as a basis for many image processing and computer vision tasks, providing the necessary precision and granularity in pixel intensity values.

Digital Image Histogram

A digital image histogram is a graphical representation of the distribution of pixel intensities in an image. It is constructed by counting the number of pixels that have each specific intensity value across the entire image. This histogram serves as a useful tool in analyzing the image’s contrast, brightness, and overall dynamic range.

Histogram-based Statistical Features

Histogram-based statistical features are quantitative measures derived from the image histogram. They include parameters such as the mean (average intensity), variance (spread of intensities), skewness (asymmetry of the intensity distribution), kurtosis (peakedness of the distribution), entropy (randomness or complexity of the image), and energy (uniformity). These features are utilized in various areas such as image segmentation, quality assessment, and pattern recognition.

Transcript

00:01 This question asks us to find unusual values in a couple distributions that we've been working with.

00:07 Now, the distributions looks at the distributions we saw in question four, where we had a number of different trial sizes, ends.

00:20 We want to find the unusual values, and let's start with a.

00:24 A, we were told, n is equal to 12.

00:28 If we look at the graph, it looks something like this, and i'm going to do my best at drawing a.

00:33 Smooth a distribution, it looks something like this, where this over here is 12, and this is zero.

00:41 Unusual values are the values that are represented the least in the distribution, but still possible.

00:49 So even though 13 and 14 don't show up at all in our distribution, they're not unusual values because they were completely impossible from the get -go.

01:00 The unusual values that we see are the ones that have very little to almost no bar over them.

01:07 That includes numbers like zero all the way up through four before we start seeing a real bar over the values.

01:17 So the unusual values for part a, we can say are zero, one, two, three, and depending on where you draw the line, you could call it four.

01:30 Now part b was the same sort of thing, but in this time we had a distribution with n equal to 4.

01:37 Now when i draw this, it looks a little bit different...

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