Here's the problem - I want to generate the delta of a binary file (> 1 MB in size) on a server and send the delta to a memory-constrained (low on RAM and no dynamic memory) embedded device over HTTP. Deltas are preferred (as opposed to sending the full binary file from the server) because of the high cost involved in transmitting data over the wire.
Trouble is, the embedded device cannot decode deltas and create the contents of the new file in memory. I have looked into various binary delta encoding/decoding algorithms like bsdiff, VCDiff etc. but was unable to find libraries that supported streaming.
Perhaps, rather than asking if there are suitable libraries out there, are there alternate approaches I can take that will still solve the original problem (send minimal data over the wire)? Although it would certainly help if there are suitable delta libraries out there that support streaming decode (written in C or C++ without using dynamic memory).
Maintain a copy on the server of the current file as held by the embedded device. When you want to send an update, XOR the new version of the file with the old version and compress the resultant stream with any sensible compressor. (Algorithms which allow high-cost encoding to allow low-cost decoding would be particularly helpful here.) Send the compressed stream to the embedded device, which reads the stream, decompresses it on the fly and XORs directly (a copy of) the target file.
If your updates are such that the file content changes little over time and retains a fixed structure, the XOR stream will be predominantly zeroes, and will compress extremely well: number of bytes transmitted will be small, effort to decompress will be low, memory requirements on the embedded device will be minimal. The further your model is from these assumptions, the less this approach will gain you.
Since you said the delta could be arbitrarily random (from zero delta to a completely different file), compression of the delta may be a lost cause. Lossless compression of random binary data is theoretically impossible. Also, since the embedded device has limited memory anyway, using a sophisticated -and therefore computationally expensive- library for compression/decompression of the occasional "simple" delta will probably be infeasible.
I would recommend simply sending the new file to the device in raw byte format, and overwriting the existing old file.
As Kevin mentioned, compressing random data should not be your goal. A few more comments about the type of data your working with would be helpful. Context is key in compression.
You used the term image which makes it sound like the classic video codec challenge. If you've ever seen weird video aliasing effects that impact the portion of the frame that has changed, and then suddenly everything clears up. You've likely witnessed the notion of a key frame along with a series of delta frames. Where the delta frames were not properly applied.
In this model, the server decides what's cheaper:
complete key frame
delta commands
The delta commands are communicated as a series of write instructions that can overlay the clients existing buffer.
Example Format:
[Address][Length][Repeat][Delta Payload]
[Address][Length][Repeat][Delta Payload]
[Address][Length][Repeat][Delta Payload]
There are likely a variety of methods for computing these delta commands. A brute force method would be:
Perform Smith Waterman between two images.
Compress the resulting transform into delta commands.
Related
I am using the Videogular2 library within my Ionic 3 application. A major feature of the application is the ability to seek to different places within a video.
I noticed that some formats have very quick seek response, while others take seconds to get there, even if the video is in the buffer already - I assume this may depend on the decoding process being used.
What would the best compromise be in order to speed up seek time while still keeping the file size reasonably small so that the video can be streamed from a server?
EDIT
Well, I learned that the fact that my video was recorded in the mov format caused the seek delays. Any transcoding applied to this didn't help because mov is lossy and the damage must have been done already. After screen-capturing the video and encoding it in regular mp4, the seeking happens almost instantaneously.
What would the best compromise be in order to speed up seek time while
still keeping the file size reasonably small so that the video can be
streamed from a server?
Decrease key-frame distance when encoding the video. This will allow for building a full frame quicker with less scanning, depending on codec.
This will increase the file size if using the same quality parameters, so the compromise for this is to reduce quality at the same time.
The actual effect depends on the codec itself, how it builds intermediate frames, and how it is supported/implemented in the browser. This together with the general load/caching-strategy (you can control some of the latter via media source extensions).
I have an int16_t[] buffer with PCM raw audio data and I want to apply some effects (like echo, reverb, gain...) into it.
I thought that SoX or similar can do the trick for me, but SoX only works with files and other similar libraries that supports adding sound effects seems to add the effects only when the sound is played. So my problem with this is that I want to apply the effect to the samples into my buffer without playing them.
I have never worked with audio, but reading about PCM data I have learned that I can apply gain multiplying each sample value, for example. But I'm looking for any library or relatively easy algorithms that I can use directly in my buffer to get the sound effects applied.
I'm sure there are a lot of solutions to my problem out there if you know what to look for, but it's my first time with audio "processing" and I'm lost, as you can see.
For everyone like me, interested in learning DSP related to audio processing with C++ I want to share my little research results and opinion, and perhaps save you some time :)
After trying several DSP libraries, finally I have found The Synthesis ToolKit in C++ (STK), an open-source library that offer easy and clear interfaces and easy to understand code that you can dive in to learn about various basic DSP algorithms.
So, I recommend to anyone who is starting out and have no previous experience to take a look at this library.
Your int16_t[] buffer contains a sequence of samples. They represent instantaneous amplitude levels. Think of them as the voltage to apply to the speaker at the corresponding instant in time. They are signed numbers with values in the range (-32767,32767]. A stream of constant zeros means silence. A stream of constant -32000 (for example) also means silence, but it will eventually burn your your speaker coil. The position in the array represents time, and the value of each sample represents voltage.
If you want to mix two sample streams together, for example to apply a chirp, you get yourself a sample stream with the chirp in it (record a bird or something). You then add the two sounds sample by sample.
You can do a super-cheesy reverb effect by taking your original sound buffer, lowering its volume (perhaps by dividing all the samples by a constant), and adding it back to your original stream, but shifting the samples by a tenth of a second's worth of array position.
Those are the basics of audio processing. Things get very sophisticated indeed. This field is known as "digital signal processing" and there are plenty of books on the subject.
You can do it either with hacking the audio buffer and trying to do some effects like gain and threshold with simple math operations or do it correct using proper DSP algorithms. If you wish to do it correct, I would recommend using the Speex Library. It's open source and and well tested. www (dot)speex (dot)org. The code should compile on MSVC or linux with minimal effort. This is the fastest way to get a good audio code working with proper DSP techniques. Your code would look like .. please read the AEC example.
st = speex_echo_state_init(NN, TAIL);
den = speex_preprocess_state_init(NN, sampleRate);
speex_echo_ctl(st, SPEEX_ECHO_SET_SAMPLING_RATE, &sampleRate);
speex_preprocess_ctl(den, SPEEX_PREPROCESS_SET_ECHO_STATE, st);
You need to setup the states, the code testecho includes these.
I have a set of mp3 files, some of which have extended periods of silence or periodic intervals of silence. How can I programmatically detect this?
I am looking for a library in C++, or preferably C#, that will allow me to examine the sound content of these files for the silences.
EDIT: I should elaborate what I am trying to achieve. I am capturing streaming sports commentary using VLC and saving it to mp3. When a game is delayed, or cancelled, the streaming commentary is replaced by a repetitive message saying commentary is not available. By looking for these periodic silences (or total silence), I can detect if there is no commentary and stop the streaming recording
For this reason I am reluctant to decompress the mp3 because if would mean my test for these silences would be very slow. Unless I can decode the last 5 minutes of the file?
Thanks
Andrew
I'm not aware of a library that will detect silence directly in the MP3 encoded data, since its not a trivial task to detect silence without first decompressing. Luckily, its easy to find libraries that decode MP3 files and access them as PCM data, and its trivial to detect silence in PCM Data. Here is one such Library for C# I found, but I'm sure there are tons: http://www.robburke.net/mle/mp3sharp/
Once you decode the data, you will have a list of PCM samples. In the most basic form, the algorithm you need to detect silence is simply to analyze a small chunks (could be as little as .25s or as much as several seconds), and make sure that the absolute value of each sample in the chunk is below a threshold. The threshold value you use determines how 'quiet' the sound has to be to be considered silence, and the chunk size determines how long the volume needs to be below that threshold to be considered silence (If you go with very short chunks, you will get lots of false positives due to samples near zero-crossings, but .25s or higher should be ok. There are improvements to the basic approach such as using historesis (which is basically using two thresholds, one for the transition to silence, and one for the transition from silence), and filtering.
Unfortunately, I don't know a library for C++ or C# that implements level detection off hand, and nothing immediately springs up on google, but at least for the simple version its pretty easy to code.
Edit: Also, this library seems interesting: http://naudio.codeplex.com/
Also, while not a true duplicate question, the answers here will be useful for you:
Detecting audio silence in WAV files using C#
My line of work requires the use of DICOM files. Each DICOM file constitutes many .dcm files in a single directory. I am required to send these files over the network, a process which is somewhat so due to the massive size of the files.
I am also a programmer and I was wondering what is the ideal way to compress such files? I'm talking about a compression that will be made on the local computer and later decompressed on the destination computer (namely the compression is solely for speeding up the over-the-network transfer of the file). Is there a simple way to crop the DICOM files? (the files contain imaging of an entire head, whereas I'm only interested in a small part of the head).
Thanks!
In medical context, lossy compression is somewhere between not encouraged and forbidden. If you'd insist on cropping existing datasets the standard demands you to form at least new image & series UIDs. The standard does allow losless compression in the form of jpeg2000, but it is quite rare - if I had to bet I'd say your dataset is uncompressed altogether.
In my experience it is significantly better to compress a medical dataset as a solid archive - that is, unify all the images into a single stream. This makes a lot of sense, as there is typically a lot of similarity between nearby images and this is the way to take advantage of that similarity (a unified compression dictionary). This is available as a command line option both to rar and gzip compressors.
Solution:
gdcmconv --jpeg uncompressed.dcm compressed.dcm
or for better compression ratio:
gdcmconv --jpegls uncompressed.dcm compressed.dcm
See:
http://gdcm.sourceforge.net/html/gdcmconv.html
I would also recommend against lossy compression, you would need to be a DICOM wizard to do it properly (see derivation mechanism in the DICOM standard). I would also recommend against cropping the image (you would need to regenerate UIDs, get the Frame or Reference updated...)
HTH
You could use something simple like lzma compression on one end to pack up the files and send them over. This is the easiest solution, since you can grab something like gzip and pack/unpack the files easily programmaticly. This may help considerably, because modern computers prefer transmitting/receiving one large file over many small files (a single 1GB file will transfer much faster than 10000 100KB files).
As for actually reducing the aggregate size, each .dcm file is probably a slice (if you're looking at something like MRI or CT data), and the viewer you are using reconstructs the slices into the 3d image. Cropping them isn't impossible, but parsing the DICOM format is a bit tricky. I'm not aware of any free programs that will help you parse the DICOM files, but I haven't looked for some time.
Since DICOM is a container format, the image data you are after is usually stored in a common format (such as JPEG), so if you are able to grab the relevant part of the file to extract the image data, you can use any of the loads of image processing tools available to crop the image to whatever dimensions you choose.
We have a compression router called "DICOM Shrinkinator" that can do this as it transmits the study to PACS:
http://fluxinc.ca/medical/dicom-shrinkinator/
Is there any way to determine a removable drive speed in Windows without actually reading in a file. And if I do have to read in a file, how much needs to be read to get a semi accurate speed (e.g. determine whether a device is USB2 or USB1)?
EDIT: Just to clarify, USB2 and USB1 were an example. These could be Compact Flash, could be SSD, could be a removable drive. And I am trying to determine this as fast as possible as it has a real effect on the responsiveness of the application.
EDIT: Should also clarify, this has to be done programatically. It will probably be done in C++.
EDIT: Boost answer is kind of what I was looking for (though I haven't written any WMI in C++). But I need to know what properties I have to check to determine relative speed. I don't need exact speed (like I said about the difference in speed between USB1 and USB2), but I need to know if it is going to be SLLOOOOWWW.
WMI - Physical Disks Properties is an article I found which would at least help you figure out what you have connected. I foresee things heading toward tables equating particular manufacturers and models to speeds, which is not as simple a solution as you may have hoped for.
You may have better results querying the operating system for information about the hardware rather than trying to reverse engineer it from data transfer timing information.
For example, identical transfer speeds don't necessarily mean the same technology is being used by two devices, although other factors such as seek times would improve the accuracy, if such information is available to your application.
In order to keep the application responsive while this work is done, try doing the calls asynchronously and provide some sort of progress indicator to the user. As an example, take a look at how WinDirStat handles this progress indication (I love the pac-man animation as each directory is analyzed).
Several megabytes, I'd say. Transfer speeds can start out slow, and then speed up as the transfer progresses. There are also variations because of file sizes (a single 1GB file will transfer much faster than 1GB of smaller files).
Best way to do that would be to copy a file to/from the device, and time how long it takes with your code. USB1 speed is 11Mb/s (I think), and USB2 is 480Mb/s (note those are numbers for the whole bus, not each port, so multiple devices on the same bus will change the actual numbers).
Try TerraCopy and copy one large file ~400mb - 500mb from device and to the device and you'll see the speed.
In Windows you can determine if a connected USB device is USB2 by selecting View -> "Devices by Connection" from the Device Manager and then checking to see if the device is under a USB2 controller (USB2 Enhanced Host Controller).
Note that this doesn't mean your device will actually perform at the higher speeds though, you would still need actual throughput tests for that. The Sisoft Sandra benchmarking software lists removable hard drives as supported in its feature list.
EDIT: Due to clarification in original question, I have submitted a new answer.
Consider the number of things that could affect data transfer speed:
The speed of the bus used to connect the device to the system. This is unlikely to be your bounding factor unless it's connected via USB1.
For hard drives, rotational speed and seek time matter. 7200 RPM drives will read and write blocks of data faster than 5400 RPM drives.
Optical and magnetic drives usually spin down when not in use, so the first access will take orders of magnitude more than the second access.
The filesystem used on the particular device.
Caching of data and filesystem metadata. The less metadata is cached, the more a magnetic or optical drive has to seek to figure out where the data is.
Data access pattern. Accessing a small number of large, contiguous files is almost always faster than accessing a large number of small files scattered around the disk.
File system fragmentation
You might be able to work up some heuristics based on the various characteristics of the devices you expect to see, but in general there's no good way to figure out transfer speed for a particular combination of bus, media, filesystem, and data access pattern without actually measuring it. If you decide to measure, try to simulate your final access pattern as closely as possible.
I'm going to borrow Raymond Chen's crystall ball and say that you really don't want this. You probably want to use asynchronous I/O. If you do not get the result of your I/O within a second, you want to check how much did happen. Take the inverse of that number, and you have a good estimate to quote to the user.
If nothing happened after a second, you may be in for a surprise. But even that can happen. For instance, a harddisk may need a second to spin up. Just poll every second until something has happened.