It never ceases to amaze me how much computation goes into things we take for granted and how inconceivably fast it happens over and over again...

This channel is researching and (perfectly) presenting exactly what you think in your spare time

As somebody who has worked on JPEG implementations, I am still amazed by how perfect they got it on the first try. JPEG has been around for ages, and is still so good; only recently have meaningful replacements emerged, and JPEG still has some benefits over these.

Up until now, I just thought JPEG lumps some similar colours together to save on file size. This was literally eye-opening. What an algorhythm!

You cover topics that I've always wanted to know and in the right amount of detail.🥰

as someone whos on youtube a lot, these still manage to be some of the best videos. The research, editing, the pace of the lesson. Thank you guys for the hard work

It's truly impressive how you managed to explain these complex processes in less than 20 minutes! The clarity and conciseness of your explanations made understanding these concepts much easier. Thank you!

Great explanation on JPEG. I knew it compressed in blocks, and I vaguely knew it averaged color, but seeing this level of detail is amazing. Well done!

I have a degree in computer science and I'm still amazed how good your channel is! Always wondered how JPEG compression worked. You have illustrated it in super simple terms and visualizations have as always been awesome! Happy to see more content like that, maybe more details on H.264.

There's just nothing like these videos, really. I'm blown away every time. The voice-over and writing are just so far beyond anything else out there. I wish all information could be presented in this calm, clear, and concise manner than also somehow manages to never dumb it down.

An absolute masterpiece of explanation. I've tried to understand how DCT works in JPEG before, and never understood the dry maths-only explanation. This video changed that and the image tables explained it perfectly. Thanks for filling in a gap that's been in my head for many years!

As someone who has studied image and video compression at university, I can say this is a GREAT video and it's spot on. It explains the basics in just the right amount of detail to get a general understanding. Would have helped me a lot in the beginning.

Great Video, just a little feedback on chroma subsampling: The reason why the human eye can perceive more luminance resolution than chroma isn't because of the number of rods vs cones. The rods are actually inactive in all but the darkest lit environments, and their density in the fovea i.e. the center of the visual field on the retina is actually rather low. So the RGB cone cells are solely responsible for tristimulus color vision. The actual reason for the color resolution difference is because there is some amount of cell level processing happening in the eye before the signal reaches the visual nerve. This is very much analogous to the Lightness chroma separation done in digital images. Also, there is some correlation between spatial resolution of each of the red, green and blue cones in terms of the lightness but little correlation between them in terms of the color. Those effects together cause the lower chroma resolution of the human visual system.

Another factor that compression works so well is that your brain all the time fills in the «gaps» in information. Especially when watching video. Our eyes is actually worse than you think. The brain does a huge amount of work filling in various gaps in information. For example each of our eyes has a blind spot. We usually do not see it since the brain erases it and fills in the missing information

really love the little details, the secondary animations in each text header for each step in compression being unique to that compression technique itself. praise your mograph artist/animator/editor!

The is the best and clearest explanation of JPEG compression that I have seen anywhere - great job!

18 minutes to teach us what our smartphones does in miliseconds. Awesome!

It is incredible how complex things that we take for granted on a daily basis can be

I never thought that there was so much computing behind each image. I am truly humbled by your work team. Thanks!

Came across your channel this week. Must say that your explanations are exceptional in the sense that they convey technical details very clearly and also very quickly grab the attention of the viewer. Exceptional i tell you. Keep it up. You should he used as the benchmark for teachers and lecturers all around the world.

This is... AMAZING. It's probably almost impossible to explain this even just marginally better. The amount of well-considered effort you go through to make things more clear is extremely impressive. Subbed!

Your videos are SPECTACULAR! It's obvious that you put a lot of work into them, and it shows. You seem to intuitively know how to achieve the perfect balance of technical detail and higher-level concepts to enable the viewer to develop a solid overall understanding of the subject matter. THANK YOU!

usualy videos like this claim to explain something but they just talk about it and dont explain anything .... but THIS video does actualy explain the techniques behind it i love it ... those vids are rare and hard to find .thank you for all the work you put into this

Underrated Chanel, this channel has everything you would like to know with details, good content. Keep going 👍🏻

One of the best educational channels on KZfaq!

Since the very 1st video I watched, this channel is one of a very few that I clicked the bell button without even thinking twice, each subject is presented with great clarity, beautifully illustrated and great narration as well. well planned, edited and organized by key topics.

Been through multiple articles and videos about JPEG compression, I must say this is one of the best!

Wow how have I not discovered this channel before. The production values are off the charts !!

Every time I remain surprised by the amount of quality and work behind your videos, wow

I never thought I can understand the basic of JPEG algorithm in less than 20 minutes. Brilliant video!

I'm a programmer and I've never bothered deep diving into jpeg, always treating it like some kind of magic. This just cleared my mind on everything about it in less than 10 minutes. I can now explain it to people, and it's almost worrying how fast this went. Damn, you're good.

This channel is a treasure to preserve, it covers modern and intriguing topics that are both mesmerizing and reaching for curiosity in technological mindsets, I love it.

I am amazed by the 3D transitions between different 2D text/diagram sections. This form of presenting learning feels closer to learning in VR. I wonder if one day this channel will create VR episodes which we can move around in. Great delivery of a new way of learning!

I love this channel! Super informative, great presentation and one with the best content in KZfaq. Thanks for making those videos and please keep up the good work!

I watched so many of your videos but this jpeg algorithm for image compression is so complicated that no one can understand in first time but you are amazing

I am really amazed by the research and hard work you put into making this video. Subscribed !!

WOW this is so detailed! So much more information than most videos will tell you. Down to a fundamental level, not just “it works better”. Thanks! I’ve been interested in how video/pictures are compressed

Amazing video as always!! The detail of explanation is precise and technically correct. I think that, once understood how JPEG works, MPEG should be not very difficult to understand. If you plan on doing videos about electric engineering topics, I'd love to see a video about OFDM access, and how it is used in 4G for letting hundreds of users connect at the same time at an e-Node B. Can't wait for the next video though!

Incredibly well explained and visualized. Thank you so much! I like the slower pace because it helps me to understand/think through what you say (but I'm no native speaker).

16:28 Well, vector images are a different beast altogether. They aren't rasterised, so _any_ bitmapping format will result in quality loss, even bmp and png. Vectors are made to be scaled and work by having nodes and lines that make up the image. Instead, what you probably meant, was that jpg doesn't perform well on digital images, such as pixel art, or anything with sharp contrast, such as text overlaying on any other image. Jpg also isn't preferred when working with intermediate copies. If you have a photo that you want to use in a video, for example, where it may undergo additional colour grading, you do not want to use a jpg image. This is because any additional grading can highlight artefacts, and because the image would be compressed twice; once by the jpg algorithm and again by the video compression.

Ah, JPEG. The greatest friend of a web designer, the worst enemy of a digital artist.

Nice, love details and simplicity in explanation of how it work. Small tip for far future video idea: accelerometers and gyro/magnetic sensors.... I always loved to read on how they managed to make those sensors.

Mind-boggling amount of work goes into things we take for granted!

I'm mechanical engineer. I've learn fourier transform since Bachler degree , one of the most unintuitive subject in my opinion... And this video, i think this is one of the most intuitive way to explain "DCT" discrete cosine transform. Great work !

Great video, I basically knew everything as my research is in video compression, I love the fact that he said video compression is extremely complex while giving an example of H.264, which is quite old now, it's still the most used for sure, but the newest codec VVC (versatile video coding) is times more complex than even HEVC which is newer than H.264, my work is in VVC, believe me it's a nightmare, don't take the videos you watch on the internet for granted ;)

Even though I always knew the basic idea behind compression this video finally gave me some real understanding - well done for a very complex subject

Back in the early 1990's I designed and worked on 'Slowscan' security systems over dialup modems. This was in the early days before the JPEG standard was published. We used our own Discrete Cosine Transform (DCT) based image compression, empirically tuned for the product's target usage. We actually used a hardware circuit to detect when an 8x8 block had changed enough to mark it as 'to be updated'. Due to the low processor computing power available at the time, the DCT was performed by an INMOS DCT/IDCT transform chip. The compression stage was left to the modem's built-in algorithm, such as V42bis.

Digital imagery takes up a great deal of my time for what I do, and I know from practice what file formats and compression do, but I never really looked into how they work. Thank you!

The amount of efforts you are putting in your video is amazing

Fantastic video, as an electronics engineer its pretty amazing to see topics such as frequency response being used everyday in our lifes

Fascinating stuff. I still remember when JPEGs first came out and waiting for my DOS machine to encode and decode them. It took some time back then and not so instant as it is today. I think the two file formats that made the biggest impact in sizes that I recall in my life were JPEGs and MP3s.

I'm a photographer and I always wanted to know this. I didn't understand anything. But great video. One day I will watch it again. Hopefully I will, then, understand it.

JPEG stands for “Joint Photographic Experts Group”. It's a standard image format for containing lossy and compressed image data.

and they say perfection isn't achievable. this is an honestly amazing video and now I'm going to write a jpg parser and decompressor just for fun because you made it. I also do embedded development, and a microprocessor I was working with had hardware jpg decoding - yet I never really thought it could be something this simple because when I googled it all these complicated terms came up. I didn't stop to think that hardware implementation are usually only feasible with simple algorithms. but now I'll know better in the future I've also been aching to implement h.264 decoding on an fpga for direct network playback (so I don't need a nasty closed source firmware raspberry pi to do it), so I can't wait for that video too! I've been trying to read up on the subject but good resources are essentially non-existent. this video alone already helped me a lot in that regard, so even if you only release the h264 one in a few months and I'm done with at least a simple software implementation by then - I'll still watch it just because your videos are great, and much of that will still be owed to this very video right here. thank you!!!

Beautiful video! Great transitions, intuitive and relaxing

Hey really thanks man! I was tod to take a seminar for mpeg conversion even though I saw many articles in Wikipedia and watched many videos I can't able to understand but your video about jpeg conversion have given me enough knowledge to explain the concept of mpeg conversion

I was wondering why I was subscribed to this channel then after a minute in I remembered how well the bluetooth explanation you(your team) have made. Thanks, the explanation is topnotch (imo of course) but there were few graphical error and some color selection could have been better.

You can clearly see those artifacts in H.264 compression in an MKBHD video where he re-uploaded the same video for 1000 times.👍

Lol I just got shown this random channel with great quality narration, animation and explanation. Subscribed

Quality oozes out of these videos. Thank you. Amazing work!

Also, you should take a super deep-dive into how Laserdisc works. I get the basics of it, but still would be nice to dig into the actual nuts and bolts of the format. :D

This video is great and I would really like to see a follow up video for another popular image format called PNG, which is orders of magnitudes better than JPEG for vector images. The reason being that it is so widely supported while having other strengths and weaknesses compared to JPEG.

This is incredible. Easy like, easy subscribe. Time to go through your entire video history!

I really appreciate it. This tutorial video was perfect. So far I have watched more than 10 videos to understand the JPG process and your video was more beneficial than those videos. Good Job man!

Phenomenally well explained as usual!

Can't wait for the x264 and x265 video, how P (Prediction) frames, I (Intra Frames) and B (Bidirectional Frames), AQ-Mode, AQ-Strength, block-size, B-Pyramid, etc., options work, how well it compresses data and why sometimes x264/x265 compress dark scenes badly along with artifacts or why x265 tends to blur out certain scenes while failing to apply proper DCT and quantization to such scenes properly to try and do an effective job at the compression or blur without any perceptive detail loss (which I believe such codecs need improvement in such area). Really need in-depth research video on it for innovation of future technologies!

I just came here to say I perfectly understood how JPEG works from thumbnail . I love you branch education so high class

I like compressed photos. Every time I see such a small amount of data that can express such rich photo content, I feel very comfortable.❤

Those signal processing algorithms behind the scenes are amazing. Especially the transforms that have been taught in the colleagues should not be rely on the mathematical calculations, instead there should be more visual explanations like this. Amazing video by the way!

I remember when JPEG first came into existence and it would take forever to compress and decompress that you would see the image go from a pixely mess into a nice image. This was the only format of image shared across BBS's over dialup, that and GIF. Bitmaps (BMP's) could be found rarely but it would take a decade to download. This by far is the most handy format for quickly sharing images.

Amazing content, as usual ! This channel is pure gold !

உங்கள் மதிப்புமிக்க அறிவைப் பகிர்ந்தமைக்கு நன்றி. Thank you for sharing your valuable knowledge.

Compression is amazing technology. I remember when the Surge 1 was been released. It was only 6gigs or so but the game was really large with many enemy types, a lot of music and many weapons and amour. I was wrecking my brain trying to figure out how in the world could they fit such a large game into such a small file size. I am still at a loss ist at how they did it. Even with this upload, it still feels like magic haha

my brain hurts

The information and the presentation is just amazing! Great work!

I love the way you explained this man. You earned a sub

Good thing to emphasize about the dct is that it's also reversible -- you get the same amount of information out as you put in, just arranged in a different way. (I explain this to myself by noticing that the frequency samples are all linearly independent -- none of them can be added to each other to make a third, no redundancy -- so you've switched from one 8x8 block of data to another 8x8 block of data.) This sort of thing (fourier transforms and cousins) are super important for signal processing

as a person working with image processing, this is absolutely fascinating. Also, that is a great explanation of DCT!

I'm so glad that because of some great people out there, this channel exists.👏👏

I was expecting a normal KZfaq video that rushes through everything and makes no sense, but wow was I wrong, these videos are easily understandable yet also really in depth.

I'm glad you mentioned Tom Scott when talking about Hoffman algorithm, I watched that video, and also the interframe compression that makes any confetti video look ugly😂

the JPG at the beginning has richer contrast and deeper colors

I've just seen the video and realized that you released the video just two days after I submitted my bachelor thesis about forensic jpeg algorithms :D Would have been great to have these visualizations back then but nonetheless great and simple explanation with really good animations!

Wow I'm so happy and excited for this video... videos that give deep understanding like this are rare.

I must say that 10:1 compression ratio using JPG is quite impressive. For special images that are easily prone to artifacting, I just use the highest quality setting and that pretty much does the "trick". It would have been nice had they included a lossless option in original JPG for people that want a mathematically identical representation of the original image, but 1/2 to 1/3rd the size of a 24 bit RGB image. So basically getting it down to the filzesize of 8 or 12 bit color, but still retaining the original 24 bit color information.

Well put together and very structured. I'd be interested to know what caused the change in the audio quality on the voice over (example 7:44 / 7:46).

Absolute best explanation I´ve ever seen... ever, ever! 👍

BEST CHANNEL EVER FOR EDUCATIONAL CONTENT

Legend has it that JPEG was the result of computer nerds getting aggravated with getting lady pictures over slow dial up. So they designed an efficient algorithm to make high quality pictures take up less space. And JPEG is the result of this. But that could be hearsay. Great video by the way. 👏

Due to the square/rectangular artifacts associated with lower-quality instances of the format, I'd always assumed JPG worked in some recursive way where it divides the image up into quadrants, throws out the least-used/prominent colors in each, and gets more "aggressive" about throwing out colors with each subdivision of quadrants, as the smaller the area the harder a discrepancy is to notice.

Thank you so much for this video, my professor wasn't really explain DCT and Qunatization to me, but the visuals used in your video helped so much, and I feel like I finally understand it!

Learned to appreciate the engineering behind the jpeg today. Very cool stuff!

Great video! JPEG is actually significantly simpler than I thought, thanks to your explanation. Would be great to have a video outlook into machine-learning based methods and the future of compression.

My questions is: How did we come up with what data needs to be thrown away in JPEG compression algorithm if our eyes couldn't perceive any of those data points at the first place? 🤔

Thanks for explaining the quantization table...

I haven't had my mind blown like this in quite a while. Partially also because of the fantastic presentation.

Just some weeks ago I purchased the HEIC plugin for Windows and my mind is blown. The file size is only about one third of that of a JPG file (but depends on the content of course) and I can't even see any artifacts when I zoom in. Most of the times I use Q80 for JPG but with HEIC Q40 is absolutely okay even with high frequency images like dark tree branches and twigs against an almost white sky during winter.

Hi, at 9:20 before Quantization, I'm having trouble understanding the Base image concept. Is one base image itself a 8X8 grid of pixels thereby meaning , each base image has 64 values. So, does size of one base image correspond to size of one block, matching exactly same 64 pixels? Is it right to think that each reconstructed block is basically a stacking(sum) of all base images multiplied with their respective constant ? Block = b1 x c1 + b2 x c2 + ....+ b64 x c64 where b - base image and c is their respective number of times.

This is the clearest, yet most comprehensive, explanation of JPEG compression that I've seen so far. I finally feel like I understand it. Thank you! One small quibble that is unrelated to the topic of JPEG compression: The video suggests that the rod cells in the human eye act something like the "luminance" channel of human vision to the cone cells "RGB" channel. The reality is quite a bit different. Rod cells are the "low light level" cells of the human eye, and do not contribute much to our visual perception in bright conditions. Indeed, bright lighting conditions, such as those of a regular phone or computer screen, are bright enough to overwhelm the rod cells and stop them from contributing a signal to our vision. They essentially "turn off" in such conditions. Conversely The cone cells are the "bright light level" cells and do not perform well in low light conditions. Also, the rod cells, though more numerous, do not work in isolation. In low light conditions, the human eye performs a kind of "binning" with rod cells, where it combines the signal from a large number of individual cells to produce a signal that passed on to the brain. Indeed, this binning can combine the light detection capabilities from so many cells that the resolution that we get in low light conditions (where the light-loving cone cells lose sensitivity) is far lower than one would expect given the number of cells involved. Try reading a printed page by moonlight alone and you'll see what I mean. (There's"temporal" binning as well, where the early processing of the eye's signal combines the light over a longer period, up to about 1/6th of a second, to produce a stronger signal and any movement during that time will "smear" the image.) So, when dealing with anything displayed on a computer screen, it is the "bright light" cone cells which do the heavy lifting. This does not affect the sharpness of our vision, however. That there are only "about 6 million cone cells" (and astonishingly few "blue" cone cells, BTW) is made up for by the fact that they are largely crammed into a tight area at the center of our vision known as the "fovea". The fovea covers only a few degrees of our vision- so the resolution in that region is quite high. Indeed, I've seen estimates that suggest if we spread the resolution of our fovea over our entire visual field, the result would be the equivalent of a 100 megapixel image or more. (TV manufacturers set their recommended viewing distance based on this sort of calculation, BTW.) Our perception of sharpness over a broad image is created by the fact that our eyes dart around (that is, perform "saccades") picking up bits and pieces of an image and assembling them in our brains. The parts of the image that our eyes do not land on are "filled in" with plausible sharpness or ignored. Try reading the paragraph above this one while staring fixedly at the period at the end of this sentence. (Don't cheat!) If you're a normal human being you can't do it, despite the fact the text above this paragraph is quite close to the center of your vision. Your eyes do not have many cone cells outside the range of that period and there's simply not enough retinal resolution there to make out the fine details of printed letters. You do not sense anything amiss, however, because your brain "fills in" the blurry areas with a "sense" of sharpness. The intensity levels are simply a measurement of the intensity of each cone cell's intensity- with a quite a bit of "local contrast enhancement" (a very interesting process, which would require many paragraphs of explanation to outline) done by the retina. Incidentally, the cone cells are not truly RGB (red, green, blue)- in that they have peak sensitivities evenly distributed across the visible spectrum. They are, instead, more like Y-YG-B (yellow, yellow-green, blue), but, with three different ranges of peak sensitivity, they are able to unambiguously distinguish all the colors of the rainbow. The rest of the details in the video which deal with our perception of spatial resolution and frequency is on point. It just doesn't have anything to do with the distinction between rod and cone cells.

incredible, I would never have thought that there is so much complexity behind a jpeg image