I have a question that's very specific, yet very general at the same time. (Also, I don't know if this is quite the right site for this.)
The Scenario
Let's say I have an uncompressed video vid.avi. It is then run through [Some compression algorithm], which is lossy. I want to compare vid.avi and the new, compressed file to determine just how much data was lost in the compression. How can I compare the files and how can I measure the difference between the two, using the original as the reference point? Is it possible at all? I would prefer a generic answer that will work with any language, but I would also gladly accept an answer that's specific to a language.
EDIT: Let me be more specific. I want something that compares two video files in a similar way that the Notepad++ Compare plugin compares text files. I just want to find out how close each individual pixel's colour is to the original file's colour for that pixel.
Thanks in advance, and thank you for taking the time to read this question.
It is generally the change in video quality that people want to measure when comparing compression methods, rather than a loss of data.
If you did want to measure somehow the data loss, you would have to define what you mean by 'data' and how you wanted to measure it. Video compression is quite complex and the approach may even differ frame by frame within a video. Data could mean the colour depth for each pixel, the number of frames per second, whether a frame is encoded based on a delay to other frames etc.
Video quality is subjective so the reduction in quality after compression will not be an absolute value. The usual way to measure the quality is similar to the technique used for audio - Mean Opinion Score: https://en.wikipedia.org/wiki/Mean_opinion_score. Its essentially uses a well defined process to try to apply some objectivity to a test audiences subjective experience.
Related
Lets say I have an image called Test.jpg.
I just figured out how to bring an image into the project by the following line:
FILE *infile = fopen("Stonehenge.jpg", "rb");
Now that I have the file, do I need to convert this file into a bmp image in order to apply a filter to it?
I have never worked with images before, let alone OpenCl so there is a lot that is going over my head.
I need further clarification on this part for my own understanding
Does this bmp image also need to be stored in an array in order to have a filter applied to it? I have seen a sliding window technique be used a couple of times in other examples. Is the bmp image pretty much split up into RGB values (0-255)? If someone can provide a link on this item that should help me understand this a lot better.
I know this may seem like a basic question to most but I do not have a mentor on this subject in my workplace.
Now that I have the file, do I need to convert this file into a bmp image in order to apply a filter to it?
Not exactly. bmp is a very specific image serialization format and actually a quite complicated one (implementing a BMP file parser that deals with all the corner cases correctly is actually rather difficult).
However what you have there so far is not even file content data. What you have there is a C stdio FILE handle and that's it. So far you did not even check if the file could be opened. That's not really useful.
JPEG is a lossy compressed image format. What you need to be able to "work" with it is a pixel value array. Either an array of component tuples, or a number of arrays, one for each component (depending on your application either format may perform better).
Now implementing image format decoders becomes tedious. It's not exactly difficult but also not something you can write down on a single evening. Of course the devil is in the details and writing an implementation that is high quality, covers all corner cases and is fast is a major effort. That's why for every image (and video and audio) format out there you usually can find only a small number of encoder and decoder implementations. The de-facto standard codec library for JPEG are libjpeg and libjpeg-turbo. If your aim is to read just JPEG files, then these libraries would be the go-to implementation. However you also may want to support PNG files, and then maybe EXR and so on and then things become tedious again. So there are meta-libraries which wrap all those format specific libraries and offer them through a universal API.
In the OpenGL wiki there's a dedicated page on the current state of image loader libraries: https://www.opengl.org/wiki/Image_Libraries
Does this bmp image also need to be stored in an array in order to have a filter applied to it?
That actually depends on the kind of filter you want to apply. A simple threshold filter for example does not take a pixel's surroundings into account. If you were to perform scanline signal processing (e.g. when processing old analogue television signals) you may require only a single row of pixels at a time.
The universal solution of course to keep the whole image in memory, but then some pictures are so HUGE that no average computer's RAM can hold them. There are image processing libraries like VIPS that implement processing graphs that can operate on small subregions of an image at a time and can be executed independently.
Is the bmp image pretty much split up into RGB values (0-255)? If someone can provide a link on this item that should help me understand this a lot better.
In case you mean "pixel array" instead of BMP (remember, BMP is a specific data structure), then no. Pixel component values may be of any scalar type and value range. And there are in fact colour spaces in which there are value regions which are mathematically necessary but do not denote actually sensible colours.
When it comes down to pixel data, an image is just a n-dimensional array of scalar component tuples where each component's value lies in a given range of values. It doesn't get more specific for that. Only when you introduce colour spaces (RGB, CMYK, YUV, CIE-Lab, CIE-XYZ, etc.) you give those values specific colour-meaning. And the choice of data type is more or less arbitrary. You can either use 8 bits per component RGB (0..255), 10 bits (0..1024) or floating point (0.0 .. 1.0); the choice is yours.
I am looking for some advice for a good way to detect either square or circular objects in an image. I currently have a canny edge algorithm running on the original greyscale and I can produce this output:
http://imgur.com/FAwowr1
Now I can see that there is a cubesat in this picture, but what is a good computationally efficient way that the program can see that aswell? I have looked at houghs transform but that seems to be very computation heavy. I have also looked at Harris corner detect, but I feel I would get to many false positives, for I am essentially looking to isolate pictures that contain said cube satellite.
Anyone have any thoughts on some good algorithms to pursue? I am very limited on space so I cannot use any large external libraries like opencv. (This is all in C btw)
Many Thanks!
I would into what is called mathematical morphology
Basically you operate on binary images, so you must find a clever way to threshold them first , the you do operations such as erosion and dilation with some well selected structuring element to extract areas of interest in your image.
I'm interested in different algorithms people use to visualise millions of particles in a box. I know you can use Cloud-In-Cell, adaptive mesh, Kernel smoothing, nearest grid point methods etc to reduce the load in memory but there is very little documentation on how to do these things online.
i.e. I have array with:
x,y,z
1,2,3
4,5,6
6,7,8
xi,yi,zi
for i = 100 million for example. I don't want a package like Mayavi/Paraview to do it, I want to code this myself then load the decomposed matrix into Mayavi (rather than on-the-fly rendering) My poor 8Gb Macbook explodes if I try and use the particle positions. Any tutorials would be appreciated.
Analysing and creating visualisations for complex multi-dimensional data is complex. The best visualisation almost always depends on what the data is, and what relationships exists within the data. Of course, you are probably wanting to create visualisation of the data to show and explore relationships. Ultimately, this comes down to trying different posibilities.
My advice is to think about the data, and try to find sensible ways to slice up the dimensions. 3D plots, like surface plots or voxel renderings may be what you want. Personally, I prefer trying to find 2D representations, because they are easier to understand and to communicate to other people. Contour plots are great because they show 3D information in a 2D form. You can show a sequence of contour plots side by side, or in a timelapse to add a fourth dimension. There are also creative ways to use colour to add dimensions, while keeping the visualisation comprehensible -- which is the most important thing.
I see you want to write the code yourself. I understand that. Doing so will take a non-trivial effort, and afterwards, you might not have an effective visualisation. My advice is this: use a tool to help you prototype visualisations first! I've used gnuplot with some success, although I'm sure there are other options.
Once you have a good handle on the data, and how to communicate what it means, then you will be well positioned to code a good visualisation.
UPDATE
I'll offer a suggestion for the data you have described. It sounds as though you want/need a point density map. These are popular in geographical information systems, but have other uses. I haven't used one before, but the basic idea is to use a function to enstimate the density in a 3D space. The density becomes the fourth dimension. Something relatively simple, like the equation below, may be good enough.
The point density map might be easier to slice, summarise and render than the raw particle data.
The data I have analysed has been of a different nature, so I have not used this particular method before. Hopefully it proves helpful.
PS. I've just seen your comment below, and I'm not sure that this information will help you with that. However, I am posting my update anyway, just in case it is useful information.
I'm doing a project with a lot of calculation and i got an idea is throw pieces of work to GPU, but i wonder whether could we retrieve results from GLSL, if it is posible, how?
GLSL does not provide outputs besides what is placed in the frame buffer.
To program a GPU and get results more conveniently, use CUDA (NVidia only) or OpenCL (cross-platform).
In general, what you want to do is use OpenCL for general-purpose GPU tasks. However, if you are insistent about pretending that OpenGL is not a rendering API...
Framebuffer Objects make it relatively easy to render to multiple outputs. This of course means that you have to structure your processing such that what gets rendered matches what you want. You can render to 32-bit floating-point "images", so you have access to plenty of precision. The biggest difficulty is what I stated: figuring out how to structure your task to match rendering.
It's a bit easier when using transform feedback. This is the ability to write the output of the vertex (or geometry) shader processing to a buffer object. This still requires structuring your tasks into something like rendering, but it's easier because vertex shaders have a strict one-vertex-to-one-vertex mapping. For every input vertex, there is exactly one output. And if you draw GL_POINTS, it's not too difficult to use attributes to pass the data that changes.
Both easier and harder is the use of shader_image_load_store. This is effectively the ability to read/write from/to arbitrary images "whenever you want". I put that last part in quotes because there are lots of esoteric rules about data race conditions: reading from a value written by another shader invocation and so forth. These are not trivial to deal with. You can try to structure your code to avoid them, by not writing to the same image location in the same shader. But in many cases, if you could do that, you could just render to the framebuffer.
Ultimately, it's pretty much impossible to answer this question in the general case, without knowing what exactly you're trying to actually do. How you approach GPGPU through a rendering API depends greatly on exactly what you're trying to compute.
I'm doing video processing tasks and one of the problems I need to solve is choosing the appropriate encoding algorithm for a video that has just one static image throughout the entire video.
Currently I tried several algorithms, such as DivX and XviD, but they produce 3MB video for a 1 minute long video. The audio is 64kbit/s mp3, so the audio takes just 480KB. So the video is 2.5MB!
As the image in the video is not changing, it could be compressed really efficiently as there is no motion. The image size itself (it's a jpg) is just 50KB.
So ideally I'd expect this video to be about 550KB - 600KB and not 3MB.
Any ideas about how I could optimize the video so it's not that huge?
I hope this is the right stackexchange forum to ask this question.
Set the frames-per-second to be very low. Lower than 1fps if you can. Your goal would be to get as close to two keyframes (one at the start, and one at the end) as possible.
Whether you can do this depends on the scheme/codec you are using, and also the encoder.
Many codecs will have keyframe-related options. For example, here are some open-source encoders:
lavc (libavcodec):
keyint=<0-300> - maximum interval between keyframes in frames (default: 250 or one keyframe every ten seconds in a 25fps movie.
This is the recommended default for MPEG-4). Most codecs require regular keyframes in order to limit the accumulation of mismatch error. Keyframes are also needed for seeking, as seeking is only possible to a keyframe - but keyframes need more space than other frames, so larger numbers here mean slightly smaller files but less precise seeking. 0 is equivalent to 1, which makes every frame a keyframe. Values >300 are not recommended as the quality might be bad depending upon decoder, encoder and luck. It is common for MPEG-1/2 to use values <=30.
xvidenc:
max_key_interval= - maximum interval between keyframes (default: 10*fps)
Interestingly, this solution may reduce the ability to seek in the file, so you will want to test that.
I think this problem is related to the implementation of video encoder, not the video encoding standard itself.
Actually, most video encoder implementations are not designed for videos of static image, thus it will not produce perfect bitstream as we imagined when a video of static image is inputted. Most video encoder implementations are designed for processing "natural" video.
If you really need a better encoding result for video of static image, you may do a hack on an open source video encoder, from 2nd frame on, mark all MBs' as "skip"...