El concepto de compresión de datos es erróneo. Y el límite de la entropía de la información de Shannon se refiere a una representación de la información como si fuese materia. Los datos no pueden "comprimirse" porque no son materiales, no son materia. Los datos son número, y el concepto debe ser exclusivamente el de "codificación", no de compresión... Es la materia la que es susceptible de "compresion"... Shannon habla de "entropía de la información" como si se tratase de materia, de átomos desordenados... Es una representación mental errónea para la información transmisible, que es número y debe ser tratada y entendida como tal. La operaciones matemáticas pueden utilizarse solamente con los números. No son aplicables a la materia ni a otros tipos de símbolos del lenguaje que se hable, escriba o represente. Sumar, restar, multiplicar, dividir, etc. La solución al problema de la compresión (no limitada) de datos vendrá de la comprensión de esta cuestión, cuando se termine de entender que aquí hablamos de números, no de materia, y evitemos representar la información como si fuesen átomos...🫡
This is really old method. Mostly today are used JPEG compression and MPEG2 or MPEG4+H264.265 compression for images and video frame compression and prediction. But yes you could compress image with this method. It is lossless compression so it would work for images that have low detail and edging,
I'm genuinely happy when I finally find that video that is not in broken English.
Vasilian Sotirov omg this is the best comment ever. I relate to this so much 🤣
You've said it all
best presentation...........simple and straight forward.......
ya good presentation
Easy, short and clear. Thank you!
El concepto de compresión de datos es erróneo. Y el límite de la entropía de la información de Shannon se refiere a una representación de la información como si fuese materia.
Los datos no pueden "comprimirse" porque no son materiales, no son materia.
Los datos son número, y el concepto debe ser exclusivamente el de "codificación", no de compresión... Es la materia la que es susceptible de "compresion"...
Shannon habla de "entropía de la información" como si se tratase de materia, de átomos desordenados...
Es una representación mental errónea para la información transmisible, que es número y debe ser tratada y entendida como tal.
La operaciones matemáticas pueden utilizarse solamente con los números. No son aplicables a la materia ni a otros tipos de símbolos del lenguaje que se hable, escriba o represente. Sumar, restar, multiplicar, dividir, etc.
La solución al problema de la compresión (no limitada) de datos vendrá de la comprensión de esta cuestión, cuando se termine de entender que aquí hablamos de números, no de materia, y evitemos representar la información como si fuesen átomos...🫡
much better than clg teachers 😂😂😂😂😂 BTW thnx it helps alot 👌👍
thanks a lot man for this vid, really helps me out a lot when trying to revise information transmission theory
Short and straight to point. Thanks!
Your teaching way is very easy and very simple.you explained this topic in a very short time.
Thankx man.. U solved my confusion about partitioning the symbols..
Thank you sir, short and clear.
Straight to the point. Thanks.
it was at this moment i knew i was gonna pass mcmaster 4j03. lovely
Dan K I get that feeling right this exact moment
Really helpful...thnx for the simple representation...
Very nice explanation , short and precise.
it was great explanation. thanks a ton
this should be shown at all teacher orientations
Well explained!
You're awesome ! Great explanation !
great explanation , thanks
Short and clear! Thank you!!
isnt there a fault? in your first step the most equal propability would be 0,5 ( 30+20 ) and 0,5 (0,25 + 0,05 + 0,12 + 0,08)
+roook88 That is not in descending order. the order *must* be in descending order
+Maths Resource ah okay! thankgs for the fast answer! got finals next week
+Maths Resource had i not read this comment, i mightve screwed up tmrw's exam!
thank you this was very helpful and easy to understand
thx so much! clear and well explained!
Thank you...I learned it easy
easy and effective explanation
Thank you very much!
Thank you so much sir!!
nicely explained ,thx man!!
Need video on how to get the number back to the letter
so thankful to you
Hvala brate! Pozdrav brate!
it was very helpful thank you
what if two partitions give us the same two values?
for example one parts as 0.1 and 0.15 and the other parts as 0.15 and 1.
Dear first decending order me karo iske bad next step equality chek karo
Thank you sooooo much
excellent and clear
very helpful thanks a lot
Thank you so much
thanks it really helped.. i have a doubt- does the no. of stages depends on the no. of symbols ? are the stages (X-2) [X=no. of symbols] ?
Thank you so much!!!!!!!!!!!
thanks mate. saviour
Nice video
at the time in video 3:03 assingned value is confusing i.e. has been interchanged
tks u so much !!
can we compress image also using shanon fano elias coding ? please reply
This is really old method.
Mostly today are used JPEG compression and MPEG2 or MPEG4+H264.265 compression for images and video frame compression and prediction.
But yes you could compress image with this method. It is lossless compression so it would work for images that have low detail and edging,
good job dude
it was best explanation
Good job
nice one, thanks!
Nicely Explained!
Thnx need more exemple please?
I have found some great data compression explanation here: ua-cam.com/play/PLLX0OlcGRiD6UHdTkgXxxHyhVEw6sX8Al.html
It may help you too.
what if all symbols have same probability??
clear. thanks
thanks teacher
Thank you
woww those are literally help mee
superb
thx that's really helpful
Please upload lampel ziv code with example explain
I have found some great data compression explanation here: ua-cam.com/play/PLLX0OlcGRiD6UHdTkgXxxHyhVEw6sX8Al.html
It may help you too.
Its very nice
Thanks!
thanks
awesome
thanks you
thanks man
thnx
tnx g
Easy!
Thanks teacher
Thanks
thanks you
awesome