That's what I was thinking ,too !! Much lesser time than others which is incredibly needed for us as the most people don't wanna spend more than 15 ,or 10 minutes to watch videos on phone !! Some are super busy . 👍👍💜🥁🐉🎤🎶💞
Though I couldn't understand the complete explanation due to my lack of the knowledge on this particular subject, I got how it relatively woks :) Thank you for this video!
duuude amazing content. My class covered this topic in like 2 seasons, 2h in total and you broke it down to 7 min. Well, to be fair it is also more detailed than this but nice overview of basic principles
you are so much great my professor only teach the compression technique but dont even explain when and how and why they are used m thanks to you i understand this now
your illustrations are very relatable...your voice is so clear...thank you for the videos....can you do a video on different image formats and different colour spaces?
I forgot why I wanted to watch the video or why it was open in the first place. But well, I learned a lot and it was a very nice video.... win-win I guess :D
very good explanation, will watch again to understand all the points. please look at fractal compression because it is as good at least for compression rate but have no information loss
The information about frequency dependant contrast sensitivity is way too interesting. Would you please link more resources? I am mostly interested in the fact that it varies from person to person and as my curve peak is moved quite a bit to the right, I would want to know why.
It's indeed interesting. I've seen it in an image processing class taught by my doctoral advisor Alan Bovik. If I remember correctly, it was also covered in his book titled "The Essential Guide to Image Processing." You can also do a web search on the Contrast Sensitivity Function find more information about it.
Wow, that's well explained. The only thing I think is (kinda) wrong: It should not be Megabyte (MB), but instead Mebibyte (MiB). Because Megabytes are base 2 which means 12MB = 12.582.912 Bytes, and Mebibyte are base 10 which means 12MiB = 12.000.000 Bytes. Another fix would be to say that the original image is 4.096x3.072 pixels... However, this mistake is in almost every Literature, so it doesn't actually matter and, moreover, it's not the point of the video... ❤
Interesting. thumbs up. So in 6:50 you have 8x8 (=64 cells) which values can be from 0 to 255 in color range for each layer of color. So if you have every possible combination besides zig zag the Permutation total would be :o over 130 digits long. Again that is just for a 8x8 with 255 numbers for one color in total. What if your compression program was 1 gigabyte then would that mean the compressed file would be smaller since the program will have all the combinations sorted from highest to low. In the end its compression /speed ratio. but it all seems to come down to luck if the numbers show up just right. for example its easier to compress a number like this 3,486,784,401 (10 digits) to divided it by 9 at 9 times to be 1 digit (3 digits long total) 66% shrink down. Even at higher digits it becomes even more efficient over 80%. But to even remove 1 random digit from the number the math is off and good luck getting even 1/2 the efficiency. multiple techniques have to be used which one key thing many are not using which would help the most is to have the file be set up so that it can be knocked down to be compressed. Again this 10 digit 3,486,784,401 is faster and better to compress than this 5 digit 84,401. Better to have a bigger file structured properly than to have a smaller disorderly file. But then again who pays attention.
Well the more those blocks getting quantized/blurred based on the frequenccy’s to remove, the more more blurrier the image will be,you could use sharpening to compensate for that but still.
I am doing a project for memristor based hardware accelerator for image compression .In this project i using xilings ise design software and matlab .But in xilings ise desing 14.5 how to change when no of inputs given and change speed , area, delay plzzz tell .e
It’s possible to get a better compression than jpeg while preserving the perceptual image quality using more computationally expensive methods. I never tried using fractal image compression but it seems to rely on self similarly. Searching for similar image patches can require a lot of computation. It’s very hard to beat well established image compression methods without increasing computational complexity.
Hi, when scaling down images, sharpness is lost. A large image with 25% quality setting is sharper than a small image with 75% quality setting. However, I'm afraid that my google pagespeed score and seo rating will down (as pagespeed prefers correctly sized images)
I really enjoyed this video. Thank you so much ! Just what is the effect of compression on noisy image (for example an image with gaussian noise). Thanks
Sure! Noise usually makes images harder to compress. A noisy image would have a larger size and lower quality after compression as compared to a clean image.
@@leoisikdogan Do you happen to know why early jpgs had very poor quality whites? In so many jpgs back in the 90's the color white nearly always came out blueish or dingy. I just saw it again on a video game from 1995ish... it was the Sony Interactive logo and all the whites were very off color. Just curious if you knew. I can't find anyone else talking about that.
Hi ! Thank you so much for your help! I was wondering, how did you manage to separate the Y, Cb and Cr images ? I have been searching all ver the internet but I can't find it.
Hi! If you are using OpenCV in Python, then you can do so by: img = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB) Y = img[..., 0] Cr = img[..., 1] Cb = img[..., 2]
@@leoisikdogan Thanks ! But I don't know openCV unfortunately. I guess I was hopping for a easier photoshop solution. But I mean, you are capable of creating paintings with AI so ... this might explain why it's also hard to create this kind of pictures !
You can do it in Photoshop too. You can find them in the channels window next to the layers. You may need to change the color space from RGB to Lab first.
