Video encoded as emojis⁉️ Stored in a DB? 😱🤯

OK, even I think I have gone too far this time!

Can we convert video to emojis (silly) and then save those emojis in a DB (a row per line of “pixels”) and serve the “image” from the DB…apparently yes!

Should you do this? No.

Was it fun? A bit.

Will it make you smile? I hope so, it was a little more work than I though it was going to be!

To give you an idea of how silly this was, I had to:

convert a video to frames at a low resolution
read each of those frames and get the pixel data
convert that pixel data into emojis
store those emojis in a DB (Postgres using Supabase)
create a stored procedure to retrieve a frame so I could make API calls in quick succession to play a “video”.

There is a lot to unpack here, and some interesting things we can learn along the way, but first…the demo!

Click the big play button in the following CodePen to see it in all of it’s glory!

Demo

WARNING: This will use 20MB of data, so if you are on 4G, don’t press play if you have an expensive data plan!

The most amazing part of that experiment is the fact that I am calling each frame individually, one after the other from Supabase, pretty impressive latency I have to admit!

More on how I store the data (and how I created it) later.

One last note before the demo

There is a reason we don’t store video as emojis, it takes up loads of space.

As the above demo is on the free tier of Supabase it may suddenly stop working as 5GB of egress will only allow the video to be played 250 times before I use too much data!

If you press play and nothing happens, I probably hit that limit, here is a video of what you would have seen:

Interesting parts of this experiment.

*As always, this is not a tutorial. *

But, there were a few interesting things along the way that may be useful for reference:

Converting and storing the data

There were a few interesting things to consider here.

You see, storing pixel data as emojis is not very efficient (I eluded to this early).

So we needed to find a way to minimise the amount of data we stored.

Once I converted the video into frames and down-sampled it I was left with this:

And there were a couple of problems with that.

Removing the black bars.

Ahhh the 80s, where TVs were nearly square and mullets were fashionable.

Unfortunately some things have changed since then.

We now use 16:9 vs 4:3 aspect ratios, which means we have some huge black bars to contend with.

There is no point storing those as emojis.

Now, we will cover retrieving the pixel data later, but all you need to know is I retrieve it a row at a time and loop through each pixel on a row.

So in order to remove those black bars there were many things I could have done here.

But I like simple and my solution was this:

As my image is 256 pixels wide, I simply skip over the first and the last 36 pixels, so I only process the 182 pixels on each row that have meaningful data.

Is it ugly? yes!

Is it inefficient? Yes!

Does it work? Also yes!

Removing some row data.

The other issue I had was size. (that’s what she said…😱)

Even with the black bars removed I still had to store 182 pixels x 144 rows = 26,208 pixels!

The problem with this is that each pixel would be stored as 4 emojis, so we would actually need 104,832 emojis per frame. This was too much!

Luckily the answer was “simple”, we are using 4 emojis per “emoji pixel”, so we just sample 1 in every 4 pixels.

Now we can’t sample every 4th pixel, it isn’t that simple. If we did that then we would lose too much information horizontally.

So we actually sample a pixel every 2 pixels, every 2 rows.

So we sample:

That takes us down to just 6,552 pixels to sample, and with 4 emojis per pixel we are back at 23k characters per frame. That will do!

encoding the data

The next step is taking that pixel data and converting it to emojis.

Now one way I could have improved this was to take all the emojis and categorise them by colour (closest match to each RGB value).

But that was a little bit too much work for this silly experiment.

So instead what I did was pick just 5 emojis to work with: 🟥🟩🟦⬜⬛

For each pixel we would grab each of the RGB values and then based on the strength, either show a coloured emoji, or a black or white one.

The logic I went with in the end was as follows:

check the red value.
is it greater than 127 (half of 255).
if “yes”, then show the red emoji.
if “no”, then we show either white or black.
repeat for Green and Blue.

Now you might be wondering, where do we decide whether to show black or white?

We do that by looking at the combined values of R + G + B.

If R + G + B > 500, show the white emoji, otherwise show the black emoji.

This works surprisingly well, for such a simple and “binary” expression of colour.

Putting it all together.

So listen…before I show you this, bear in mind that this is a single use, throwaway bit of code OK.

I don’t want you doing a code review on this garbage.

We clear? Good. 🤣

There are a few things I haven’t covered that this code does:

loads an image from an image input onto a canvas and grabs the pixel data.
putting image data onto the second canvas (hint Uint8ClampedArray is important for this…I got stuck on that for a while!)
grabbing the image data itself (Another useful tip: image data from a Canvas is stored in a “flat array” in 4 byte chunks, so you have to jump 4 array items for each pixel. e.g. R1, G1, B1, A1 then R2, G2, B2, A2).
builds an ugly INSERT statement to insert our image data into a database table.

Here is an image you can download if you want to test it (as it ONLY works on this specific image size and removes black bars based on these images only.)

And here is a CodePen with the code I used to convert the frames…ugly but it works.

The last part, the database query

Honestly, this part was super simple thanks to Supabase, there are probably only a couple of points worth covering.

Storing and retrieving the data

I decided to make this a little more fun and store each line of pixels as a row in the database.

Which makes for a really fun visual:

The 2 columns we needed to make sure this worked were the frameid column (so we could query all rows for a frame) and the rownum column, so that we can order the rows of the frame.

Then it was as simple as adding a custom function in the SQL editor that retrieved this information:

It might look complicated, but if we break it down it isn’t so bad!

This first part let’s us define a reusable function. This is important as we can then use this function as an API endpoint later!

Here we define the return type. The part in brackets corresponds to the columns we are returning (if we had multiple columns we would set the name we want the column to be called and the type for each).

The last part is our SQL query (and returning it as this is a function).

The STRING_AGG function is one you might not be familiar with. All it does is join columns and characters together, so we grab the rowdata data and then combine that with CHR(13) (a new line character) to build our output string.

The important part to make this work though is the GROUP BY statement. Without this the STRING_AGG function won’t work as it won’t know what data it should aggregate together over multiple rows.

And then we just have ORDER BY – this just makes sure we get each row of data in order so the image makes sense!

Creating our query and API endpoint

The beauty of Supabase is that now we are just a step away from being able to use that function as an API endpoint.

We just need an access policy.

You can find this under “authentication” > “policies” in a project.

Now, I am fairly new to Postgres, but luckily Supabase had a useful template all set up that let me grant Read permissions.

And with that, I can now call that function as an API endpoint!

(if you aren’t sure where to get that info, it is in “Database” > “Functions” and you will see a row similar to this:

Calling our API endpoint

Last thing we need, getting our data.

For that we need our SUPABASE_URL and our key (SUPABASE_KEY).

Took me a little while as I was new to Supabase, but they are located under “Project Settings” > “API”.

Then all we need is the Supabase SDK (I got it here: https://cdn.jsdelivr.net/npm/@supabase/supabase-js@2.42.0/dist/umd/supabase.min.js) and to create a client with

Now we are ready to go!

To call an API endpoint you use .rpc.

So my function looks like this:

Where get_frames is the name of the function I created earlier and { frame: num } is the variable name and the data I want to pass in to the function.

And with that, we are done!

Video, encoded in emojis, stored in a DB, served via Supabase APIs!

You might be asking, WHY?

A valid question!

I like to learn through silly things like this.

I never really used Supabase and they asked me if I wanted to write an article for them to celebrate the Supabase launch week.

So I wanted to try them out. (in retrospect, doing something “normal” might have been a better idea so I didn’t eat through my 5GB of free egress data, which would be very generous for most applications but not for this! haha).

I am sure when they asked me to write they were expecting a tutorial, or something useful, but we all know I don’t do that!

Additionally, I also wanted to learn a little more about the <canvas> element and how to manipulate pixel data, as I have an even sillier idea using that (Doom 3…in the browser…in emojis – yes, I am serious! 🤣).

Anyway, hopefully you learned a little bit too, but if not, I hope you at least enjoyed my silliness.

Oh, and last thing: I rick rolled you, in emojis.

That is a huge W for me and an even bigger L for you I reckon, so that is another reason why I did this! 🤣💗

Have a great week everyone and let me know what you thought in the comments.