The main difference between DCT and PCA (more precisely, representing a dataset in the basis formed by the eigenvectors of its correlation matrix - also known as the Karhunen Loeve Transform) is that the PCA must be defined with respect to a given dataset (from which the correlation matrix is estimated), while the DCT is "absolute" and is only defined by the ...
Monocular video is taken using a single camera. Stereo video is taken using two cameras side-by-side, producing two video streams. That's pretty much it.
A stereo video pair allows depth measurement, so you can find out how far away things are. However, this is not an easy task for complex scenes.
Before elaborating anything, i would urge you to go through my answer here that simplifies some of the common confusions prevailing about motion vectors and estimations.
Your essential question is - when or how does encoder decides to put B frame or bidirectional motion vectors. I will break your questions into few as follows:
1. When does encoder decides ...
[EDIT: addition of a March 2017 preprint]
Deep learning already has many applications in video, like enhancement (Deep Convolutional Neural Network for Decompressed Video Enhancement) or semantic analysis.
Recently, there have been some announcements related to video compression, for instance:
Generative Compression (Preprint, March 2017)
I think the following code can help you:
The main idea is that you need to decode frames first and query motion frames by calling av_frame_get_side_data function.
There are many ways to skin this cat. I'll explain one way that I know is used in industry.
Create candidate frames algorithmically using the image features; e.g., the histogram, sharpness, presence of humans, faces, and anything else you can think of.
Serve different candidates to different users and see which ones are popular
You can stop once the ...
The 3-digit number describes the subsampling of the
chroma (U and V) channels. A detailed explanation is at
In particular, YUV420 means that the U and V channels have half the resolution of the luma (Y) channel, in the horizontal and vertical directions. The sampling method usually present in low to medium ...
The total delay is directly related to minimum delay that needs processing at encoder and decoder before which the first frame is ready to render at the receiver.
The process of the transmission and decoding will happen as follows: The IBBPBBI with index (0,1,2,3,4,5,6) will be compressed and transmitted in the patterns - as IPBBIBB (0,3,1,2,6,4,5). Given ...
The MPEG4 video codec allows for a large ranges (easily a dynamic range of 50 for "useful" quality) of compression ratios for the same raw video material. The same applies to audio compressors.
And of course, there's simply video material that is easy to compress, so using the same type of quantization, motion prediction and frame structure, some ...
For actual implementations take a look at (C) programming codes for decoders.
Basically the process is simple, you create an abstraction such as
bits = get_bits(n)
Where, n is the n bits you want to read. the var bits is left aligned. It extracts it from some byte_buffer - say a 32 bit or 64 bit symbols.
NOTE: All, practical codecs put major symbols ...
Usually, Salt and Pepper Noise is done using Median Filter.
There are variations of the Non Local Means to deal with Salt and Pepper but it might be overkill so I'd start with the above.
The tricky thing is to take advantage of having a Video and not just a static single image.
Hence you can also apply the filter in a temporal form.
If you use small kernel ...
Theoretically, VQ is always better than scalar quantization (SQ). However, as you already mentioned, the complexity of VQ is higher than the complexity of SQ. It increases exponentially together with the dimensionality of vectors. Therefore, the question is how to design a practical coding system which can provide a best rate-distortion performance under a ...
Don't discount the contribution of physics to the blur in your pictures: Especially in low light, you need to have long exposure times, to get a picture that isn't completely dominated by grain. Conversely, things become less sharp if they're moving.
In fact, security cameras tend to have low frame rates (that might actually be for historical reasons – ...
I think you have in general the correct idea. But you have to remember that the encode order (and therefore the order in which the pictures will appear in the bitstream and be decoded), depends on how the references are.
if you have: I(1) B(2) B(3) P(4) B(5) B(6) P(7). etc...
You could have a structure where in the group [B(2) B(3) P(4)], the first B(2) ...
Well video compression is essentially changing the representation of the video into an intermediate one, such that this representation could be used to recover the original video to the best possible extent. What you store is a much shorter representation instead of the full video.
Well, putting it that way immediately reminds me of the (Convolutional) Auto ...
There are several tools to analyze video streams which which can provide slice and macroblock level information:
Intel Video Pro Analyzer
Tektronix Video Analyzer
ffmpeg (page Analyzing Macroblock Types)
From How extract Intra-Prediction modes out of h264 stream, it seems that ffmpeg or ffprobe can do a lot of jobs.
I think there are multiple things to consider here:
1) Where are you dividing the image into 8x8 blocks? If you don't divide like this, then normalization by 64 is meaningless, and you are just computing SAD over all pixels. Maybe I'm seeing something wrong? You should do the normalized SAD in those small windows and then sum and normalize. And since 3600 ...
It is possible to extract the transform coefficients without fully decoding the entire picture, but you would still have to find your way through the bit stream, parsing whatever is necessary in order to arrive at every transform unit, which might also not be present for every coding unit (if some macroblock matches exactly one in a reference picture then ...
Sparse dictionaries, dictionary learning or dictionary coding has been popular (and also shown to be quite high-performing) for noise reduction (denoising) and it is basically a kind of vector quantizer. Building the code-book (dictionary) takes time and I don't know exactly how large it typically becomes for denoising applications, but if it is calculated ...
Reduction of Noise in a video depends on what type of noise is present in the video.
The noise can be reduced by filters which are all present [There are software for this] and many type of filters such as median ,mean,wiener filter.
And The noise model can be build to add noise to the video there are many matlab code as well as software also there.