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Image Resolution And Print Quality

Written by Steve Patterson.

In this Photoshop tutorial, we’re going to look at how image resolution affects print quality.

Have you ever downloaded an image from the internet and then printed it, only to get results that were, well, less than you expected? The image looked great on your computer screen, but when you printed it, it either printed at the size of a postage stamp or it printed at a decent size but looked blurry or “blocky”? The culprit is image resolution.

Actually, that’s not really fair to say. Image resolution didn’t purposely set out to make your life miserable when you printed your internet photo. The problem was simply that most photos on the internet have very small pixel dimensions, usually in the neighborhood of 640 pixels wide by 480 pixels high, or even smaller, and that’s because images don’t need to be very large in order to appear at a decent size and good quality on your computer screen, and also because smaller images download much faster on websites than larger images do (which is a whole other topic that we don’t need to get into here).

So what can you do to make photos you download off the internet appear just as high quality when printed as photos you took yourself with your digital camera? The answer – absolutely nothing. There simply are not enough pixels in most internet images to allow them to print at high quality, at least not without printing them at the size of a postage stamp, that is. Let’s find out why.

First of all, let’s get off the topic of downloading images from the internet, since we really shouldn’t be doing that anyway without permission from the copyright owner, and look at image resolution in general. I cover it in much more detail in the Image Resolution, Pixel Dimensions and Document Size tutorial, but let’s do a short recap.

The term “image resolution” means how many of your image’s pixels will fit inside each inch of paper when printed. Obviously, since your photo has a fixed number of pixels, the more of them you squeeze inside each inch of paper, the smaller the image will appear on the paper. Likewise, the fewer pixels you print per inch, the larger the image will appear on paper. The number of pixels that will be printed per inch is known as the resolution of the image, or “image resolution”. Image resolution has everything to do with printing your image. It has nothing to do with how your image appears on your computer screen, which is why images you download off the internet usually appear much larger and higher quality on your screen than they do when you print them.

Download this tutorial as a print-ready PDF!

Let’s use a photo as an example:

An unflattering photo of a horse.

I always laugh every time I see this photo of a horse I took while driving around the countryside one day. Normally this horse stands proud, powerful, full of grace and dignity, yet I seem to have caught him in a rather unflattering moment. He’s standing on a bit of a strange angle, he has a piece of straw dangling from his hair, and he seems to be in the middle of chewing his food. Either that, or he’s desperately trying to crack a smile for me. In either case, since this guy is already embarrassed, as am I for having taken this wonderful photo, let’s use this image as an example.

First, let’s look at what Photoshop can tell us about the current size of this photo. I’ll go up to the Image menu at the top of the screen and choose Image Size, which brings up the appropriately-named Image Size dialog box:

The Image Size dialog box shows us the current size of the photo.

The Image Size dialog box is divided into two main sections, Pixel Dimensions at the top and Document Size directly below it. The Pixel Dimensions section tells us how many pixels are in our image. The Document Size section tells us how large the image will appear on paper if we print it. If we look at the Pixel Dimensions section, we can see that this photo has a width of 1200 pixels and a height of 800 pixels. That may sound like a lot of pixels (1200 x 800 = 960,000 pixels!), and it certainly would be if we were displaying this image on a computer screen. In fact, at 1200 x 800, it may be too large to fit entirely on your screen! But just because it looks nice and big on the screen doesn’t necessarily mean it will print nice and big, at least not with any degree of quality. Let’s take a closer look at what the Document Size section is telling us:

The Document Size sections tells us how large or small the photo will print based on a specific resolution.

The Document Size section of the Image Size dialog box tells us two things – what the current resolution of our image is, and how large or small the image will appear if we print it based on that resolution. Currently, our resolution value is set to 72 pixels/inch, which means that out of the 1200 pixels that make up our photo from left to right (the width), 72 of them will print inside each inch of paper, and out of the 800 pixels that make up the image from top to bottom (the height), 72 of them will print inside each inch of paper. The value in the Resolution box is for both width and height, not the total number of pixels that will print. In other words, for every square inch of paper, 72 pixels from our image will be printed from left to right and 72 pixels will be printed from top to bottom. The total number of pixels printed in every square inch of paper would then be, in this case anyway, 72 x 72 (72 pixels for the width times 72 pixels for the height), which gives us 5184 pixels!

Let’s do some simple math ourselves to make sure that the width and height being shown to us in the Document Size section is correct. We know from the Pixel Dimensions section that we have 1200 pixels from left to right in our image and 800 pixels from top to bottom. Our print resolution is currently set to 72 pixels/inch, so to figure out how large our image will be when printed, all we need to do is divide the number of pixels from left to right by 72, which will give us our print width, and the number of pixels from top to bottom by 72, which will give us our print height. Let’s do that:

800 pixels high divided by 72 pixels per inch = 11.111 inches

Based on our own simple calculations, at a resolution of 72 pixels/inch (ppi for short), our image would be 16.667 inches wide by 11.111 inches high when printed. And if we look at the Document Size section once again:

Confirming the print size shown in the Document Size section.

That’s exactly what it says! Wow, a 1200 x 800 pixel photo is large enough for an 11 x 14 inch print, with a little extra to spare! That’s great!

Sadly, no. If only life were that simple.

The fact is, 72 pixels/inch is not enough to give us sharp, good quality, professional looking images when printed. It’s not even close. To give you an idea of what I mean, here’s a rough approximation of how the photo would look on paper if we tried to print it at a resolution of 72 pixels/inch. You’ll have to use your imagination a bit here and try to imagine this at 11 x 16 inches:

The photo as it would appear on paper when printed at only 72 pixels/inch.

Doesn’t exactly look good, does it? The problem is that at 72 pixels/inch, the image information is being spread out too far on the paper for the photo to appear sharp and detailed, sort of like spreading too little peanut butter over too much toast. The photo now appears soft, dull and generally unappealing. We don’t see this problem on a computer screen because computer monitors are generally referred to as low resolution devices. Even a photo with relatively small pixel dimensions, like 640 x 480, will look great on a computer screen. Printers, however, are high resolution devices, and if you want your photos to appear sharp and detailed when printed, you’ll need a resolution much higher than 72 pixels/inch.

So how high of a resolution value do you need for professional quality printing? The generally accepted value is 300 pixels/inch. Printing an image at a resolution of 300 pixels/inch squeezes the pixels in close enough together to keep everything looking sharp. In fact, 300 is usually a bit more than you need. You can often get by with a resolution of 240 pixels/inch without noticing any loss of image quality. The professional standard, though, is 300 pixels/inch.

Let’s take our same image then at 1200 pixels wide by 800 pixels high, change our resolution from 72 pixels/inch to 300 pixels/inch, and see what we get. Here’s the Image Size dialog box again showing the new resolution of 300 pixels/inch. Notice in the Pixel Dimensions section at the top that we still have 1200 pixels for the width and 800 pixels for the height. The only thing that’s changed is our resolution, from 72 to 300:

The print resolution has been changed to 300 pixels/inch.

With our resolution now increased from 72 to 300 pixels/inch, this means that out of the 1200 pixels that make up our image from left to right, 300 of them will now print inside every inch of paper, and out of the 800 pixels contained in our image from top to bottom, 300 of them will now print inside every inch of paper. Naturally, with so many more pixels squeezing into each inch of paper, we’d expect the photo to print much smaller, and sure enough, the Document Size section is now showing that our photo will print at a size of only 4 inches wide by 2.667 inches high:

The photo will now print at a much smaller size than before.

Where did those new width and height values come from? Again, some simple math is all we need:

800 pixels high divided by 300 pixels per inch = 2.667 inches

The photo will now print much smaller than it would at a resolution of 72 pixels/inch, but what we lose in physical size, we more than make up for in image quality. At 300 pixels/inch (or even 240 pixels/inch), we’d enjoy sharp, detailed, professional quality print results:

Higher print resolutions result in smaller photos but much better image quality.

Of course, most people don’t print their photos at weird sizes like 4 x 2.667, so how do we make sure we’re going to get professional quality print results with more standard print sizes like 4 x 6? An excellent question, and the answer comes to us once again through some boring yet simple math.

Let’s say you’ve taken some photos of your recent family vacation using your digital camera and you want to print out some 4 x 6’s on your printer. We know now that in order to achieve professional quality prints, we need set the resolution of our images to a minimum of 240 pixels/inch, although 300 pixels per inch is the official standard. Let’s look at both of these resolution values though to see how large of an image, in pixels, we’ll need out of the camera in order to print 4 x 6’s with good image quality. First, let’s look at 240 pixels per inch:

To figure out how large, in pixels, our images need to be in order to print 4 x 6’s at professional quality, all we need to do is multiply 240 x 4 for the width, and then 240 x 6 for the height (or vice versa depending on if your photo is in landscape or portrait mode). Let’s do that:

240 pixels per inch x 6 inches high = 1440 pixels

Based on our math, we can see that in order to print a digital photo as a 4 x 6 at 240 pixels/inch resolution, which should give us excellent quality, our photo’s pixel dimensions need to beat least 960 x 1440. We can see exactly how many pixels that is by multiplying 960 by 1440, which gives us 1,382,400 pixels. Let’s round that up to 1.4 million pixels. That may sound like a lot of pixels but it really isn’t, not when you consider that 1.4 million is the minimum number of pixels you’d need to print good quality 4 x 6 photos using the minimum resolution we can use to achieve good quality, which is 240 pixels/inch. The good news at least is that these days, most digital cameras on the market are 5MP (“mega pixels”, or “millions of pixels”) and higher, so they’d have no trouble printing good quality 4 x 6’s even using 300 pixels/inch for the resolution.

Of course, we haven’t actually looked at how many pixels we’d need to print professional quality 4 x 6’s at 300 pixels/inch, so let’s do that now. We’ll use the same simple formula as above, where we’ll multiply 300 by 4 and then 300 by 6 to give us the pixel dimensions we’ll need:

300 pixels per inch x 6 inches high = 1800 pixels

Let’s do one more quick calculation to see how many pixels we need in total:

1200 pixels wide times 1800 pixels high = 2,160,000

So, in order to print a photo as a 4 x 6 using the professional standard of 300 pixels/inch for resolution, our photo needs to be 1200 pixels wide by 1800 pixels high (or vice versa), which means we’ll need a total of 2,160,000 pixels, which again should be no problem for most digital cameras on the market today which are 5MP and higher.

What if you have a photo you absolutely love and feel it deserves an 8 x 10 print rather than a 4 x 6? How large of an image in pixels do we need to print a good quality 8 x 10? The answer is as easy as when we needed to find out how large of an image we’d need for a 4 x 6. All we need to do is multiply the resolution value in pixels by the width in inches and do the same thing for the height. Let’s first use 240 pixels per inch as our resolution:

Total number of pixels = 1920 pixels wide x 2400 pixels high = 4,608,000 pixels

From our little bit of math, we can see that in order to print a photo at good quality as an 8 x 10, our photo needs to be 1920 pixels wide by 2400 pixels high (or vice versa), for a total of approximately 4.6 million pixels. Now we’re starting to push the limits of lower end digital cameras. A 4MP digital camera wouldn’t capture quite enough pixels to be able to print an image at 8 x 10 at 240 pixels/inch resolution. It would fall about 600,000 pixels short. You could still print an 8 x 10 image of course, but you most likely wouldn’t get professional looking results.

Let’s do the same calculations for an 8 x 10 at 300 pixels/inch resolution:

Total number of pixels = 2400 pixels wide x 3000 pixels high = 7,200,000 pixels

Now we’re really pushing the limits as far as digital cameras currently on the market. In order to be able to print a photo as an 8 x 10 using the 300 pixels/inch resolution standard, our photo needs to be 2400 pixels wide by 3000 pixels high (or vice versa), for a total of 7.2 million pixels! Now that’s a lot of pixels! This means you need at least a 7.2MP digital camera in order to be able to print your photos as 8 x 10’s and still get true, professional quality prints.

Of course, keep in mind that most photos require at least a little cropping, which means you’ll need to start with even more pixels. If you know you’re going to be printing a lot of photos as 8 x 10’s, investing in a good quality 8 MP or higher camera is highly recommended. And there we have it!

You're reading Image Resolution And Print Quality

Golang Program To Read And Print Two

What is a 2D array?

A two-dimensional array is a collection of data that are arranged in rows and columns. In go we can use the for loops to iterate and print elements of the 2d arrays.

Here is an example of the same below −

0 1 2 4 5 6 8 9 10 Method 1: Using For Loops

In this method, we will use for loops in golang to iterate over the arrays and catch each element then we will print that element unless the whole array is iterated upon.


Step 1 − Import the fmt package.

Step 2 − Now we need to start the main() function.

Step 3 − Then create a two-dimensional matrix naming matrix and store data to it.

Step 4 − Now, use two for loops to iterate over the array elements. Using the first for loop will get the row of the multi-dimensional array while the second for loop gives us the column of the two-dimensional array.

Step 5 − Once a particular matrix element is obtained, print that element on the screen and move on to the next element until the loop gets completed.


In this example, we will use for loops to read and print the elements of two-dimensional arrays by using for loops.

package main import "fmt" func main() { var array [][]int var row int var col int array = make([][]int, row) for i := range array { array[i] = make([]int, col) } array = [][]int{ {0, 1, 2}, {4, 5, 6}, {8, 9, 10}, } fmt.Println("The given matrix is:") for i := 0; i < 3; i++ { for j := 0; j < 3; j++ { fmt.Print(array[i][j], "t") } fmt.Println() } fmt.Println() } Output The given matrix is: 0 1 2 4 5 6 8 9 10 Method 2: Using Internal Function

In this method, we are going to use the range function in the first example and array slicing in the second example.


Step 1 − Import the fmt package.

Step 2 − Now we need to start the main() function.

Step 3 − Then we are creating a matrix naming matrix and assign data to it.

Step 4 − Now use the range function to iterate over the matrix elements and print each element of the matrix on the screen.

Example 1

In this example, we will use the range function of go Programming to get the elements of two-dimensional arrays with the combination of range function and for loop.

package main import "fmt" func main() { var array [][]int var row int var col int array = make([][]int, row) for i := range array { array[i] = make([]int, col) } array = [][]int{ {10, 13, 21}, {47, 54, 63}, {82, 91, 0}, } fmt.Println("The required array is:") for _, row := range array { for _, val := range row { fmt.Print(val, "t") } fmt.Println() } } Output The required array is: 10 13 21 47 54 63 82 91 0 Example 2

In this program, we will print a 2-D array by using the concept of array slicing property of go language.

package main import "fmt" func main() { var array [][]int var row int var col int array = make([][]int, row) for i := range array { array[i] = make([]int, col) } array = [][]int{ {10, 13, 21}, {47, 54, 63}, {82, 91, 0}, } fmt.Println("The required array is:") for _, j := range array { fmt.Print(j, "t") fmt.Println() } fmt.Println() } Output The required array is: [10 13 21] [47 54 63] [82 91 0] Method 3: Using Recursion

In this method, we will use the concept of recursion to read and print the elements of two-dimensional arrays on the screen.


Step 1 − First, we need to import the fmt package.

Step 2 − Now, create a recursive function called printMatrix() that accepts the multidimensional array as argument to it along with the current row index that should be printed.

Step 3 − Now, if the current row variable is equal to the length of the multidimensional array then the program will end.

Step 4 − A for loop is used to iterate over the array and print the current element. Once the current row is printed the function calls itself again by incrementing the row variable.

Step 5 − In this way all the three rows of the array are printed on the screen.

Step 6 − Now, start the main() function. Inside the main() initialize a 2-D array and assign value to it.

Step 7 − Now, call the printMatrix() function by passing the matrix along with the current row position as argument to the function.


The following code uses recursion method to to read and print two-dimensional array

package main import "fmt" func printMatrix(matrix [][]int, row int) { if row == len(matrix) { return } for _, element := range matrix[row] { fmt.Print(element, "t") } fmt.Println() printMatrix(matrix, row+1) } func main() { var array [][]int var row int var col int array = make([][]int, row) for i := range array { array[i] = make([]int, col) } array = [][]int{ {12, 13, 21}, {47, 54, 23}, {28, 19, 61}, } fmt.Println("The given matrix is:") printMatrix(array, 0) } Output The given matrix is: 12 13 21 47 54 23 28 19 61 Conclusion

We have successfully compiled and executed a go language program to read and print the elements of multi-dimensional arrays. In the first example we have only used the for loop while in the second example we have used the combination of range function and for loops. In the third example we have used the concept of recursion where we call the function from itself until the execution is completed.

Image Processing And Feature Extraction Using Python

In this article, I will take you through some of the basic features of image processing. The ultimate goal of this data massaging remains the same : feature extraction. But here we need more intensive data cleaning. But data cleaning is done on datasets , tables , text etc. How is this done on an image? We will look at how an image is stored on a disc and how we can manipulate an image using this underlying data?

Importing an Image

Importing an image in python is easy. Following code will help you import an image on Python :

Understanding the underlying data

This image has several colors and many pixels. To visualize how this image is stored, think of every pixel as a cell in matrix. Now this cell contains three different intensity information, catering to the color Red, Green and Blue. So a RGB image becomes a 3-D matrix. Each number is the intensity of Red, Blue and Green colors.

Let’s look at a few transformations:

As you can see in the above image, we manipulated the third dimension and got the transformation done. Yellow is not a direct color available in our dictionary but comes out as combination of red and green. We got the transformation done by setting up intensity of other colors as zero.

Converting Images to a 2-D matrix

Handling the third dimension of images sometimes can be complex and redundant. In feature extraction, it becomes much simpler if we compress the image to a 2-D matrix. This is done by Gray-scaling or Binarizing. Gray scaling is richer than Binarizing as it shows the image as a combination of different intensities of Gray. Whereas binarzing simply builds a matrix full of 0s and 1s.

Here is how you convert a RGB image to Gray scale:

As you can see, the dimension of the image has been reduced to two in Grayscale. However, the features are equally visible in the two images. This is the reason why Grayscale takes much lesser space when stored on Disc.

Now let’s try to binarize this Grayscale image. This is done by finding a threshold and flagging the pixels of Grayscale. In this article I have used Otsu’s method to find the threshold. Otsu’s method calculates an “optimal” threshold by maximizing the variance between two classes of pixels, which are separated by the threshold. Equivalently, this threshold minimizes the intra-class variance.

Following is a code to do this transformation:

Blurring an Image

Last part we will cover in this article is more relevant for feature extraction : Blurring of images. Grayscale or binary image sometime captures more than required image and blurring comes very handy in such scenarios. For instance, in this image if the shoe was of lesser interest than the railway track, blurring would have added a lot of value. This will become clear from this example. Blurring algorithm takes weighted average of neighbouring pixels to incorporate surroundings color into every pixel. Following is an example of blurring :

In the above picture, after blurring we clearly see that the shoe has now gone to the same intensity level as that of rail track. Hence, this technique comes in very handy in many scenarios of image processing.

Let’s take a practical example of such application in analytics industry. We wish to count the number of people in a town’s photograph. But this image has a few buildings also. Now the intensity of the people behind the buildings will be lower than building itself. Hence, it becomes difficult for us to count these poeple. Blurring in such scenarios can be done to equalize the intensities of buildings and people in the image.

Complete Code

Here is the complete code :

[stextbox id=”grey”]

image = imread(r"C:UsersTavishDesktop7.jpg") show_img(image) red, yellow =   image.copy(), image.copy() red[:,:,(1,2)] = 0 yellow[:,:,2]=0 show_images(images=[red,yellow], titles=['Red Intensity','Yellow Intensity']) from skimage.color import rgb2gray gray_image = rgb2gray(image) show_images(images=[image,gray_image],titles=["Color","Grayscale"]) print "Colored image shape:", image.shape print "Grayscale image shape:", gray_image.shape from skimage.filter import threshold_otsu thresh = threshold_otsu(gray_image) show_images(images=[gray_image,binary_image,binary],titles=["Grayscale","Otsu Binary"]) from skimage.filter import gaussian_filter blurred_image = gaussian_filter(gray_image,sigma=20) show_images(images=[gray_image,blurred_image],titles=["Gray Image","20 Sigma Blur"])


End Notes

[stextbox id=”grey”][/stextbox]

The world of image processing is already so rich that multi-billion dollar companies today rely on these image processing tools for various purposes. These image processing techniques are being used heavily in researches and automization of industry processes. In few of the coming articles we will take a deep dive into feature extraction from an image. This will include detecting corners, segmenting the image, seperating object from the background etc.

Did you find the article useful? Share with us any practical application of image processing you have worked on.  Do let us know your thoughts about this article in the box below.

P.S. Have you joined Analytics Vidhya Discuss yet? If not, you are missing out on awesome data science discussions. Here are 2 of my best picks among recent discussions:

1. How to do feature selection and transformation?

2. Algorithm for time series forecasting

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How To Resize A Video Or Change Resolution

There are a lot of reasons why you might need to resize a video and in this post I’m going to mention a couple of freeware app that will help you easily change the resolution of your videos. If you have one of those 1080p 60fps HD video cameras, you understand the need for some serious conversion. With the introduction of 4K camcorders on the market, you’ll need a beast of a PC along with a 4K monitor to even have a chance of playing that high quality

There are currently no default tools built into Windows that you can use to resize videos. You can use Windows Movie Maker to edit videos and add transitions, etc, but not to change the actual video resolution.

Table of Contents

If you do a search for RESIZE VIDEOS in Google, you’ll get a list of a bunch of commercial applications that you would have to shell out cash for! So if you’re lucky enough to have run across this post, I’ll show you a couple of free ways to resize videos quickly and easily.


HandBrake is my all time favorite utility for converting and encoding videos. Basically, it can take as input pretty much any video file you can throw at it and it gives you two options for output: MP4 and MKV. You can also pick from three video codecs for the two video containers: H.264, MPEG-4 and MPEG-2.

You’ll see the progress of the conversion at the bottom of the program window. The amount of time will depend on the original size and encoding of your video.


VirtualDub is a free video capture and video processing software app. It doesn’t have many of the video editing features that you would find in Adobe Premiere, but it is streamlined for performing linear operations on video very fast. It also has batch-processing capabilities for processing large number of video files.

You can do a TON of stuff with VirtualDub, including add special effects to your videos such as blurs, black and white, flipping, and lots more. However, we’ll only going to go over how you can use it to resize your videos. It should be noted that VirtualDub works best on AVI files and will require additional codecs installed in order to work with other files types like AVCHD, MP4, etc.

First download VirtualDub and extract it to a folder on your hard drive. VirtualDub does not require any installation; it simply runs directly via an EXE! That means it doesn’t mess around with your registry or anything else in Windows.

Browse to the location of your video and open it. You’ll now see your video appear twice in the main window. This is because the left one is considered your original and the right one is your “processed” or altered version. When you apply a filter to your video, the right one updates and you can watch both at the same time! Cool!

You should now see your original video on the left and your processed video on the right, in my case, half the size!

Freemake Video Converter

I would have recommended Freemake Video Converter over VirtualDub because it accepts so many different video formats as input, but I purposely am listing it last because the installer for the program has a lot of junkware. This is not malware or spyware, but it’s just junk that you would not want on your system.

I really like this program because it has a ton of options that will let you get your video onto pretty much any device you can think of. I really just wish they didn’t try to bundle that crapware along with the installer! You can even send the video directly to YouTube, convert it to HTML5 compatible format, convert it to Xbox and Playstation format and even burn to DVD or Blu-ray disc right from the program. If you’re an Apple user, you’ll love the to Apple option, which lets you pick the exact device you want the video to be played on.

Here Is Why Face And Image Recognition Gaining Prominence

Do you remember watching crime shows where investigating teams used to hire sketch artists to draw the image/face of criminal described by witnesses? And they would then hunt for the person to lock him up. But one might wonder today, are these tactics still common in detecting crime or criminals? Obviously not! With the rise in Artificial Intelligence enabled Face and Image Recognition technologies , the days of sketching criminal are long gone. The process of identifying or verifying the identity of a person using their face has made investigations a lot easier today. The tools and platforms empowered by facial detection technology capture, analyze and compare patterns based on the person’s facial details. As the process is a quintessential move towards detecting and locating human faces in images and videos, the technology has transformed several sectors besides crime-investigation. In fact, today face and image recognition is considered the most natural of all biometric measurements. Be it airports, offices or even schools, face and image recognition can be located in many places. Outshining fingerprint and irises detection, facial biometrics are appraised to the preferred biometric benchmark eliminating the need for any physical interaction. Moreover, the face detection is comparatively faster than other match processes. Furthermore, big techs including Google, Amazon, Apple, Microsoft, and Facebook are vying to gain supremacy in innovating biometrics through several pieces of research and projects. We can observe a trend in the regular roll-out of theoretical discoveries in the fields of image recognition and face analysis by several software giants. For example, Facebook announced the launch of its DeepFace program back in 2014. The program would determine whether two photographed faces are of the same person, with an accuracy rate of 97.25 percent. A year later, the search engine pioneer Google introduced FaceNet. And the innovation continues to evolve to date! One of the latest trends that took the face and image detection market by boom is Emotion Recognition that analyses human emotions using real-time static images. Using the process of mapping facial expressions, the technology can identify emotions such as disgust, joy, anger, surprise, fear, or sadness on a human face. However, technology is not being taken positively by many. Certain researches and researchers as well criticized the methodology used behind emotion detection algorithms claiming it as outdated. They also found that such outdated algorithms are at high risk provoking race, gender, and other significant biases. Despite all the condemnation, market reports predict that the global image recognition market size is projected to reach US$ 81.88 billion by 2026 while the facial recognition market is expected to hit US$12 billion by 2025. Owing to the accelerating embracement of AI capabilities, the demand for such tools and products has increased worldwide. Moreover, the major regions adopting face and image recognition technologies are North America, Asia-Pacific, and the Middle East. Among others, North America is expected to witness a flourishing market in the near future. It is expected to generate a revenue of US$ 31.28 billion by 2026. On the other hand, Asia Pacific is one of the fastest-growing adopters of the face and image detection capabilities in terms of CAGR. Face and image recognition capabilities are increasingly being deployed in surveillance and security systems, data validation, tracking, and data analysis giving considerate rise to investments in the research and development of innovation in this field. The technology is more likely to catalyze the growth of disruptive technologies market in several other places in the forthcoming years.

How To Check Air Quality Index On Your Iphone And Apple Watch

If you’re living in an area that’s known to have poor air quality, then it’s best to keep a track of the AQI or Air Quality Index on a regular basis. Bad air consists of pollutants, allergens, and pollens that can cause long-term health problems. The Air Quality Index reports daily air quality and ranges from 0 to 500. When it comes to AQI, lower is always better. There are plenty of ways to track AQI, and you can do so right from your iPhone or Apple Watch.

In this article, you’ll learn how to get the AQI on your iPhone and Apple Watch using the Weather app, Air Quality app, and Google Maps.

Check air quality on iPhone

Here are three ways to do that.

1. Using the Weather app

While there are plenty of third-party apps available that’ll tell you the Air Quality Index on your iPhone, you can also use the iOS Weather app.

Here’s how to check AQI on your iPhone in your region:

Launch the Weather app on your iPhone.

Select your region and scroll down.

You should be able to see the Air Quality Index of your area.

The AQI bar is displayed at the top of the page when a region is experiencing bad air quality. Some regions may not show the AQI bar if there’s no official reading in your region. If you want to view the AQI at all times on your Home Screen, then you should consider installing a third-party app.

2. Using the Air Quality Reader app

The Air Quality Reader app is a free download from the App Store, which offers a widget that shows the Air Quality map on your Home Screen and inside the app. Simply download the app, open it, and tap the colored dot to see the air quality.

3. In Google Maps

Google Maps also lets you see the air quality. To do that, open Google Maps and tap the Map button from the top right. Next, tap Air Quality and it should show you the “Air quality in this area.”

See Air Quality Index on Apple Watch

Moving on to the Apple Watch, you can use a simple Watch complication to view the AQI at all times. Here’s how to do it:

Open the Watch app on your iPhone.

Select a Watch face to add the AQI complication on.

Tap on the Complication that you want to change.

Scroll down to Weather options, and select AQI.

The watch face will now show the AQI index for your selected location, and will also update it regularly. This way, you’ll have a real-time idea of the air quality by glancing at your Apple Watch.

You can also add complications directly from your Apple Watch by pressing the watch face and choosing Edit.

Note: You are also able to see the AQI on your Apple Watch via the Weather app, you don’t really have to add it as a complication if you don’t want to.

Seeing air quality on iPhone and Apple Watch

Keeping a track of the Air Quality Index is a good idea, and now you know how to check AQI from your iPhone and Apple Watch. As mentioned above, there are other third-party apps that provide more information than the Weather app. You may want to try them out as well. We hope this tutorial helped you out.

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