If a person skilled at drawing were to attempt to represent this coded reference visually, it is likely the result would be recognizable to others as a representation of the text that is, the text is an extremely compact symbolic representation of an image.
#Assign hotkey quod libet series
The underlying mechanism is a sort of vector quantization where the text represents a series of vectors that semantically reference complex culturally shared elements that form a type of codebook. I’m sure each person reading this develops an internal model, likely some combination of a snug, warm indoor Christmas scene while outside a storm raged, or something to that effect derived from the shared cultural semantic representation: a scene with a great deal of detail and complexity, despite the very short text string. It was a dark and stormy night and all through the house not a creature was stirring, not even a mouse. Here disclosed is a novel compression technique I call Deep Learning Semantic Vector Quantization (DLSVC) that achieves in this sample 9,039:1 compression! Compare this to JPEG at about 10:1 or even HEIC at about 20:1, and the absolutely incredible power of DL image compression becomes apparent.īefore I disclose the technique to achieve this absolutely stunning result, we need to understand a bit about the psychovisual mechanisms that are being exploited.
#Assign hotkey quod libet update
If you don’t have mediainfo installed, sudo apt update Results might look like file path rate kbps size MB # above the min size of concern, and if so, print the resultįind "$1" -type f \( -iname \*.avi -o -iname \*.mkv \) -print0 | while read -rd $'\0' file # above the min rate of concern and then if the files size is # then pass each file found to check if the data rate is # the -o means "or", -iname (vs -name) means case indpendent so # search for files with the extensions enumerated below # multipliers to get to human readable valuesĮcho -e "file path \t rate kbps \t size MB" Printf "\nUsage:\n\n returning files with file size greater than default max of 100 MB \n\n"Įcho -e "\n\n returning files with dara rate greater than " $maxs " MB \n\n" Printf "\nUsage:\n\n returning files with data rate greater than default max of 100 kbps \n\n"Įcho -e "\n\n returning files with dara rate greater than " $maxr " kbps \n\n" Printf "\nUsage:\n\n pass a starting point and min data rate in kbps and min size like /media/gessel/datas/Downloads/ 100 10 \n\n" # check arguments passed and set defaults if needed Save the file as a name you like (such as find-high-rate-media.sh) and # chmod +x find-high-rate-media.sh and off you go. Piping the output to a file, output.csv, makes it easy to sort and otherwise manipulate in LibreOffice Calc. The script will then report media with a rate higher than minimum and size larger than minimum as a tab delimited list of filenames, calculated rate, and calculated size. find-high-rate-media.sh /search/path/tostart/ Files are found recursively from the starting point (passed as first argument) using find.īasic usage would be. All math is done with bc, which is usually installed. It’s different from file size detection in that it uses mediainfo to determine the media file length and wc -c to get the size, and from that computes the total effective data rate.
#Assign hotkey quod libet archive
You compressed the file some time ago in an old, inefficient format, or you just need to archive the oversize stuff, this can help you find em.
Sometimes you want to know if you have media files that are taking up more than their fair share of space.