I'm currently experimenting with webp encoder (no wic) on windows 64 environment. My samples are 10 jpg stock photos depicting landscapes and houses, and the photos already optimized in jpegtran. I do this because my goal is to optimize the images of a whole website where the images have already been compressed with photoshop using the save for web command with various values on quality and then optimized with jpegtran.
I found out that using values smaller than -q 85 have a visual impact on the quality of the webp images. So I'm playing with values above 90 where the difference is smaller. I also concluded that I have to use -jpeg_like because without it the output is sometimes bigger in size than the original, which is not acceptable. I also use -m 6 -f 100 -strong because I really don't mind about the time the encoder needs to produce the output and trying to achieve the smoother results. I tried several values for these and concluded that -m 6 -f 100 -strong have the best output regarding quality and size.
I also tried the -preset photo avoiding any other parameter except -q but the size of the output gets bigger.
What I don't understand from https://developers.google.com/speed/webp/docs/cwebp#options are the options -sns , -segments which seem to have a great impact on the output size. Sometimes the output is bigger and sometimes smaller in size for the same options but I haven't concluded yet what is the reason for that and how to properly use them.
I also don't understand the -sharpness option which doesn't have an impact at the output size at least for me.
My approach is far less than a scientific approach and more like a trial and error method and If anybody knows how to use those options for the specific input and explain them for optimum results I would appreciate such a feedback.
-strong and -sharpness only change the strength of the filtering in the header of the compressed bitstream. They will be used at decoding time. That's why you don't see a change in file size for these.
-sns controls the choice of filtering strength and quantization values within each segments. A segment is just a group of macroblocks in the picture, that are believed to be sharing similar properties regarding complexity and compressibility. A complex photo should likely use the maximum allowed 4 segments (which is the default).
Related
I am using a startech capture card for capturing video from the source machine..I have encoded that video using matlab so every frame of that video will contain that marker...I run that video on the source computer(HDMI out) connected via HDMI to my computer(HDMI IN) once i capture the frame as bitmap(1920*1080) i re-size it to 1280*720 i send it for processing , the processing code checks every pixel for that marker.
The issue is my capture card is able to capture only at 1920*1080 where as the video is of 1280*720. Hence in order to retain the marker I am down scaling the frame captured to 1280*720 which in turn alters the entire pixel array I believe and hence I am not able to retain marker I fed in to the video.
In that capturing process the image is going through up-scaling which in turn changes the pixel values.
I am going through few research papers on Steganography but it hasn't helped so far. Is there any technique that could survive image resizing and I could retain pixel values.
Any suggestions or pointers will be really appreciated.
My advice is to start with searching for an alternative software that doesn't rescale, compress or otherwise modify any extracted frames before handing them to your control. It may save you many headaches and days worth of time. If you insist on implementing, or are forced to implement a steganography algorithm that survives resizing, keep on reading.
I can't provide a specific solution because there are many ways this can be (possibly) achieved and they are complex. However, I'll describe the ingredients a solution will most likely involve and your limitations with such an approach.
Resizing a cover image is considered an attack as an attempt to destroy the secret. Other such examples include lossy compression, noise, cropping, rotation and smoothing. Robust steganography is the medicine for that, but it isn't all powerful; it may be able to provide resistance to only specific types attacks and/or only small scale attacks at that. You need to find or design an algorithm that suits your needs.
For example, let's take a simple pixel lsb substitution algorithm. It modifies the lsb of a pixel to be the same as the bit you want to embed. Now consider an attack where someone randomly applies a pixel change of -1 25% of the time, 0 50% of the time and +1 25% of the time. Effectively, half of the time it will flip your embedded bit, but you don't know which ones are affected. This makes extraction impossible. However, you can alter your embedding algorithm to be resistant against this type of attack. You know the absolute value of the maximum change is 1. If you embed your secret bit, s, in the 3rd lsb, along with setting the last 2 lsbs to 01, you guarantee to survive the attack. More specifically, you get xxxxxs01 in binary for 8 bits.
Let's examine what we have sacrificed in order to survive such an attack. Assuming our embedding bit and the lsbs that can be modified all have uniform probabilities, the probability of changing the original pixel value with the simple algorithm is
change | probability
-------+------------
0 | 1/2
1 | 1/2
and with the more robust algorithm
change | probability
-------+------------
0 | 1/8
1 | 1/4
2 | 3/16
3 | 1/8
4 | 1/8
5 | 1/8
6 | 1/16
That's going to affect our PSNR quite a bit if we embed a lot of information. But we can do a bit better than that if we employ the optimal pixel adjustment method. This algorithm minimises the Euclidean distance between the original value and the modified one. In simpler terms, it minimises the absolute difference. For example, assume you have a pixel with binary value xxxx0111 and you want to embed a 0. This means you have to make the last 3 lsbs 001. With a naive substitution, you get xxxx0001, which has a distance of 6 from the original value. But xxx1001 has only 2.
Now, let's assume that the attack can induce a change of 0 33.3% of the time, 1 33.3% of the time and 2 33.3%. Of that last 33.3%, half the time it will be -2 and the other half it will be +2. The algorithm we described above can actually survive a +2 modification, but not a -2. So 16.6% of the time our embedded bit will be flipped. But now we introduce error correcting codes. If we apply such a code that has the potential to correct on average 1 error every 6 bits, we are capable of successfully extracting our secret despite the attack altering it.
Error correction generally works by adding some sort of redundancy. So even if part of our bit stream is destroyed, we can refer to that redundancy to retrieve the original information. Naturally, the more redundancy you add, the better the error correction rate, but you may have to double the redundancy just to improve the correction rate by a few percent (just arbitrary numbers here).
Let's appreciate here how much information you can hide in a 1280x720 (grayscale) image. 1 bit per pixel, for 8 bits per letter, for ~5 letters per word and you can hide 20k words. That's a respectable portion of an average novel. It's enough to hide your stellar Masters dissertation, which you even published, in your graduation photo. But with a 4 bit redundancy per 1 bit of actual information, you're only looking at hiding that boring essay you wrote once, which didn't even get the best mark in the class.
There are other ways you can embed your information. For example, specific methods in the frequency domain can be more resistant to pixel modifications. The downside of such methods are an increased complexity in coding the algorithm and reduced hiding capacity. That's because some frequency coefficients are resistant to changes but make embedding modifications easily detectable, then there are those that are fragile to changes but they are hard to detect and some lie in the middle of all of this. So you compromise and use only a fraction of the available coefficients. Popular frequency transforms used in steganography are the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT).
In summary, if you want a robust algorithm, the consistent themes that emerge are sacrificing capacity and applying stronger distortions to your cover medium. There have been quite a few studies done on robust steganography for watermarks. That's because you want your watermark to survive any attacks so you can prove ownership of the content and watermarks tend to be very small, e.g. a 64x64 binary image icon (that's only 4096 bits). Even then, some algorithms are robust enough to recover the watermark almost intact, say 70-90%, so that it's still comparable to the original watermark. In some case, this is considered good enough. You'd require an even more robust algorithm (bigger sacrifices) if you want a lossless retrieval of your secret data 100% of the time.
If you want such an algorithm, you want to comb the literature for one and test any possible candidates to see if they meet your needs. But don't expect anything that takes only 15 lines to code and 10 minutes of reading to understand. Here is a paper that looks like a good start: Mali et al. (2012). Robust and secured image-adaptive data hiding. Digital Signal Processing, 22(2), 314-323. Unfortunately, the paper is not open domain and you will either need a subscription, or academic access in order to read it. But then again, that's true for most of the papers out there. You said you've read some papers already and in previous questions you've stated you're working on a college project, so access for you may be likely.
For this specific paper, table 4 shows the results of resisting a resizing attack and section 4.4 discusses the results. They don't explicitly state 100% recovery, but only a faithful reproduction. Also notice that the attacks have been of the scale 5-20% resizing and that only allows for a few thousand embedding bits. Finally, the resizing method (nearest neighbour, cubic, etc) matters a lot in surviving the attack.
I have designed and implemented ChromaShift: https://www.facebook.com/ChromaShift/
If done right, steganography can resiliently (i.e. robustly) encode identifying information (e.g. downloader user id) in the image medium while keeping it essentially perceptually unmodified. Compared to watermarks, steganography is a subtler yet more powerful way of encoding information in images.
The information is dynamically multiplexed into the Cb Cr fabric of the JPEG by chroma-shifting pixels to a configurable small bump value. As the human eye is more sensitive to luminance changes than to chrominance changes, chroma-shifting is virtually imperceptible while providing a way to encode arbitrary information in the image. The ChromaShift engine does both watermarking and pure steganography. Both DRM subsystems are configurable via a rich set of of options.
The solution is developed in C, for the Linux platform, and uses SWIG to compile into a PHP loadable module. It can therefore be accessed by PHP scripts while providing the speed of a natively compiled program.
;)
if you wanted encode 2mb of data onto a 2d-bar code, which 2-bar code would be good to starting point or recommend.
There are lots and different types of 2dbar codes out today,Aztec 2-d barcodes,maxicodes,Pdf417,Microsoft HCCB,vericodes....etc...lots.... all unique in their own way.
i guess in a nutshell my questions is.... which barcode would make a good start off point to encode 2mb of data??
i tried reading through the Qr code international standard turns out even # version 40L the most amount of data you could encode is on to a Qr code is
1) numeric data: 7 089 characters
2) alphanumeric data: 4 296 characters
3) 8-bit byte data: 2 953 characters
4) Kanji data: 1 817 characters
which are all a far cry from the 17million bits thats is 2mb
my goal was to create something like
http://realestatemobilemarketingsolutions.com/wp-content/uploads/2012/07/real-estate-mobile-marketing.png
After you scan the barcode you can view photos of the house/property on your phone, you dont have to walk-in or wait for an open home,20 photos # 100kb each is about 2mb
Even if you could create a single 2D barcode which will encode the whole thing, the user won't be able to scan the whole thing in one go. No one has a cellphone imager which will support that kind of resolution. Your best bet is to do a QR-code with a URL in it.
Things like DataMatrix and QR-codes are extensible. You have a limit to how much data can be encoded into one block, but you CAN create a code which has multiple blocks. Indeed, if you look at this page, you'll see a discussion of using pages full of 2D barcodes as a form of data backup. They were able to fit up to 1/2 MByte of raw data into a single page. That's at 600 dpi, which will require a scanner (not a smartphone) to decode.
From what I've been reading, DataMatrix tends to have less overhead and, therefore, will stuff more (payload) data into a square inch for a given DPI. You would need a mobile app capable of shooting multiple images (tiles) of a very large image and either:
compositing the individual images into one large one for decoding OR
decoding each of the smaller blocks and reconstructing the original data from the pieces
I know of no app which will do that.
I've pondered providing bulk data via 2D barcodes. I was pondering publishing a mobile app in a magazine and providing a way for people to "download" the app from the magazine, without needing to provide a website / FTP site where they could download it. I'd first need to provide an app which could decode such a monster. Then, the end user would have to be patient enough to scan the whole thing. Good luck with that.
I MIGHT be able to provide a large 2D barcode containing a .torrent file and then using existing BitTorrent apps to download the resulting app; I have a .torrent for a recent Linux Live-DVD where the .torrent is < 32 KB.
A chunk of data (an app or images) in the MB or larger range ... really not feasible through this channel. The megabytes of data you're wanting to provide ... again ... really not feasible through this channel.
Voiceye Code is the highest density 3d code I have been able to find. Works well too, but code making software is price prohibitive to screw around with. 500.00 (ish)
How about using some variant of DataGlyphs, which has a lot in common with steganography? In other words, you use a greyscale image to also store your data...
I have developed a reader for JAB codes that can read whole audio file from a codebar. JAB codes are very high capacity due to polychrome nature.
More on this here
I want to mix audio files of different size into a one single .wav file without clipping any file.,i.e. The resulting file size should be equal to the largest sized file of all.
There is a sample through which we can mix files of same size
[(http://www.modejong.com/iOS/#ex4 )(Example 4)].
I modified the code to get the mixed file as a .wav file.
But I am not able to understand that how to modify this code for unequal sized files.
If someone can help me out with some code snippet,i'll be really thankful.
It should be as easy as sending all the files to the mixer simultaneously. When any single file gets to the end, just treat it as if the remainder is filled with zeroes. When all files get to the end, you are done.
Note that the example code says it returns an error if there would be clipping (the sum of the waves is greater than the max representable value.). This condition is more likely if you are mixing multiple inputs. The best way around it is to create some "headroom" in the input waves. You can do either do this in preprocessing, by ensuring that each wave's volume is no more than X% of maximum. (~80-90%, depending on number of inputs.). The other way is to do it dynamically in the mixer code by multiplying each sample by some value <1.0 as you add it to the mix.
If you are selecting the waves to mix at runtime and failure due to clipping is unacceptable, you will need to modify the sample code to pin the values at max/min instead of returning an error. Don't just let them overflow or you will get noisy artifacts.
(Clipping creates artifacts as well, but when you haven't created enough headroom before mixing, it is definitely preferrable to overflow. It is a more familiar-sounding type of distortion, similar to what you get when you overdrive your speakers. See this wikipedia article on clipping:
Clipping is preferable to the alternative in digital systems—wrapping—which occurs if the digital hardware is allowed to "overflow", ignoring the most significant bits of the magnitude, and sometimes even the sign of the sample value, resulting in gross distortion of the signal.
How I'd do it:
Much like the mix_buffers function that you linked to, but pass in 2 parameters for mixbufferNumSamples. Iterate over the whole of the longer of the two buffers. When the index has gone beyond the end of the shorter buffer, simply set the sample from that buffer to 0 for the rest of the function.
If you must avoid clipping and do it in real-time and you know nothing else about the two sounds, you must provide enough headroom. The simplest method is by halving each of the samples before mixing:
mixed = s1/2 + s2/2;
This ensures that the resultant mixed sample won't overflow an int16_t. It will have the side effect of making everything quieter though.
If you can run it offline, you can calculate a scale factor to apply to both waveforms which will keep the peaks when summed below the maximum allowed value.
Or you could mix them all at full volume to an int32_t buffer, keeping track of the largest (magnitude) mixed sample and then go back through the buffer multiplying each sample by a scale factor which will make that extreme sample just reach the +32767/-32768 limits.
I am trying to use tesseract-2.04 in my iPhone application and just want to detect the numbers. What I am doing here is first I am cross compiling tesseract to generate lib file using this post http://robertcarlsen.net/2009/07/15/cross-compiling-for-iphone-dev-884 and then using the the demo application at http://robertcarlsen.net/2010/01/12/ocr-for-iphone-source-1080 , but the results far away than realistic.
I am not able to resolve the issue or how to train tesseract so that it comes closure for practical usage.
Please help.
Thanks,
Madhup
I get quite good results setting
TessBaseAPI::SetVariable("tessedit_char_whitelist", "0123456789");
while gently urging the user to let the numbers fit in a certain box. This makes locating the numbers easier for me, and ensures the user keeps the image steady and at a reasonable distance leading to a sharper image.
I have thought about altering valid_word() in tesseract-2.04/dict/permute.cpp, but there seems to be no need for that.
The next step will be to hardcode a minimum/maximum char size so recognition time can become way less than the 500 ms it is now. Then the next step will be to add some code that keeps track of results in time, so that reading 5 90% of the time and 8 only 10% will lead the code to remember the 5.
It all depends on the use case you have. I'm lucky in the sense that I'm allowed to just show a 200x50 box which will contain the number.
Is there an efficient way to get a fingerprint of an image for duplicate detection?
That is, given an image file, say a jpg or png, I'd like to be able to quickly calculate a value that identifies the image content and is fairly resilient to other aspects of the image (eg. the image metadata) changing. If it deals with resizing that's even better.
[Update] Regarding the meta-data in jpg files, does anyone know if it's stored in a specific part of the file? I'm looking for an easy way to ignore it - eg. can I skip the first x bytes of the file or take x bytes from the end of the file to ensure I'm not getting meta-data?
Stab in the dark, if you are looking to circumvent meta-data and size related things:
Edge Detection and scale-independent comparison
Sampling and statistical analysis of grayscale/RGB values (average lum, averaged color map)
FFT and other transforms (Good article Classification of Fingerprints using FFT)
And numerous others.
Basically:
Convert JPG/PNG/GIF whatever into an RGB byte array which is independent of encoding
Use a fuzzy pattern classification method to generate a 'hash of the pattern' in the image ... not a hash of the RGB array as some suggest
Then you want a distributed method of fast hash comparison based on matching threshold on the encapsulated hash or encoding of the pattern. Erlang would be good for this :)
Advantages are:
Will, if you use any AI/Training, spot duplicates regardless of encoding, size, aspect, hue and lum modification, dynamic range/subsampling differences and in some cases perspective
Disadvantages:
Can be hard to code .. something like OpenCV might help
Probabilistic ... false positives are likely but can be reduced with neural networks and other AI
Slow unless you can encapsulate pattern qualities and distribute the search (MapReduce style)
Checkout image analysis books such as:
Pattern Classification 2ed
Image Processing Fundamentals
Image Processing - Principles and Applications
And others
If you are scaling the image, then things are simpler. If not, then you have to contend with the fact that scaling is lossy in more ways than sample reduction.
Using the byte size of the image for comparison would be suitable for many applications. Another way would be to:
Strip out the metadata.
Calculate the MD5 (or other suitable hashing algorithm) for the
image.
Compare that to the MD5 (or whatever) of the potential dupe
image (provided you've stripped out
the metadata for that one too)
You could use an algorithm like SIFT (Scale Invariant Feature Transform) to determine key points in the pictures and match these.
See http://en.wikipedia.org/wiki/Scale-invariant_feature_transform
It is used e.g. when stitching images in a panorama to detect matching points in different images.
You want to perform an image hash. Since you didn't specify a particular language I'm guessing you don't have a preference. At the very least there's a Matlab toolbox (beta) that can do it: http://users.ece.utexas.edu/~bevans/projects/hashing/toolbox/index.html. Most of the google results on this are research results rather than actual libraries or tools.
The problem with MD5ing it is that MD5 is very sensitive to small changes in the input, and it sounds like you want to do something a bit "smarter."
Pretty interesting question. Fastest and easiest would be to calculate crc32 of content byte array but that would work only on 100% identical images. For more intelligent compare you would probably need some kind of fuzy logic analyzis...
I've implemented at least a trivial version of this. I transform and resize all images to a very small (fixed size) black and white thumbnail. I then compare those. It detects exact, resized, and duplicates transformed to black and white. It gets a lot of duplicates without a lot of cost.
The easiest thing to do is to do a hash (like MD5) of the image data, ignoring all other metadata. You can find many open source libraries that can decode common image formats so it's quite easy to strip metadata.
But that doesn't work when image itself is manipulated in anyway, including scaling, rotating.
To do exactly what you want, you have to use Image Watermarking but it's patented and can be expensive.
This is just an idea: Possibly low frequency components present in the DCT of the jpeg could be used as a size invariant identifier.