5

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.


5

Bit rate and data rate are somewhat ambiguous, and their exact definitions vary from one field or application to another. Bit rate is often used to measure the information rate, since information is measured in bits. Sometimes it is used to measure the number of actual 1s and 0s transmitted per second. When the information is compressed (source-coded), the ...


4

Averaging may not be the best option, due to motion compensation in video frames: one number (say $8$) may be extrapolated for another (say $3$) on a different JPEG block. It might be interesting to dig into the compressed video format, check motion vectors, but this requires a little bit of hacking. Some software which can help: Amped FIVE (used by the ...


4

The frame is 2D Grid which samples the continuous world in Discrete way. Leave alone video for a second. Let's talk about taking a photo of a white paper laid on a black surface. The photo is taken from above and let's assume there is no lens issues. Do you expect the end of the paper to end at edge of a pixel of the camera perfectly? It won't. The end ...


3

One way to do it is to solve a MAP problem of the up scaled video and using the High Resolution images as a prior. Try looking at the articles - Super Resolution MAP.


3

To OCR or not? If the timer is about $h$ pixels height, you could segment each frame into lines of height $h/2$, run an OCR on each line, and scan the result with a regexp for anything that looks like a timer, eg. \d+[\.:,;-]\d+[\.:,;-]. This gives you $x=time_{real}$ vs. $y=time_{video}$ data pairs. Sure, most of them are wrong, but imposing the ...


3

Since you already know your background image beforehand, it should be simple. I done many background subtraction before. Here is an example of removal of background by comparison. if (newBitmap.Width == backgroundBitmap.Width && newBitmap.Height == backgroundBitmap.Height) { for(int x = 0; x < newBitmap.Width; x++) { ...


3

[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) Traditional ...


3

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 ...


3

For those classic Video Processing operations there is nothing better than the Plug In's of AviSynth. Specifically for De Flickering look at: LMFlicker. ReduceFlicker. DeFlicker. The source code of each is available so you can get inspiration from them. All of them basically works on smoothing the Luma Data along the temporal dimension.


2

first of all as I understand there are two separate problems 1. Piece together Lena's beautiful face in some kind of panorama 2. Denoise the image. For 1 - the problem you described could be easily solved using cross correlation. For example please see Matlab image registration example For 2- I would use any of several denoising algorithms. You could look ...


2

If the noise levels added to each pixel are independent, perhaps blurring the images first and then applying a pairwise cross-correlation could do the job.


2

From an algorithm perspective, subtraction is the simplest method. Run a feature detection algorithm (like SIFT or SURF) on both images to align them Apply any enhancement operations at this stage, such as lens distortion removal, lighting correction, or others. Do a simple subtraction of the background image from the test image. Perform a blur or median ...


2

This is clearly not an easy task. The problem is, if you want a more-or-less accurate count, then you need to turn to advanced algorithms (and maybe use 2 cameras, or a kinect). If you can't afford to take this path, then you need to try simpler options. Personally, I would try the following: detecting skin pixels, segmenting the image with respect to ...


2

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 ...


2

Scenes generally exhibit a fade-to-black transition. You could capture those frames with simple image processing tools, which are found in many libraries, such as OpenCV. If you want to rely on the change of the mis-an-scene to be robust against sudden changes, then of cours algorithms for detecting temporal differences is more appropriate. For that, you ...


2

I think Technically speaking the only way to detect when a frame ends is to see where the next one begins. That means, you should search for the next start prefix code 0x000001. If you go to the previous data before this (which in the general case is the byte alignment syntax elements) and add your RS codes, a compliant decoder should ignore all that until ...


2

There is a company named Signalscape who has a line of products called StarWitness (http://www.starwitnessinfo.com/) that do forensic video analysis that are available to law enforcement agencies (only). I will try to get a hold of the director of the StarWitness group to determine if there is some course of action you can take that may assist you in ...


2

Thanks to helpful comments from @MBaz, I managed to come up with a solution: we can associate multiple audio samples with a single frame using the VideoFileWriter object. This fact and use-case is missing in the documentation. First, some stats about the audio and video files. The stereo audio samples are in a 2xN array signal. The video frames are in a ...


2

I assume your matrix is 3D where the first 2 dimensions are Width and Height and the third is time (Gray Scale Video). If you can write your processing as a Filter you can use MATLAB's filter() function. The function has the dim property hence you can set it to 3 and it will apply on the filter over the third dimension. The limitation is applying the same ...


2

I've used in the past Julie Delon's flickering removal (pdf) approach which is based on midway image equalization. It's not hard to implement, and the parameters are easy enough to tune. Since it seems that the brightness change impacts the whole frame, you probably don't need to use the patch oriented approach and can go for a pair-wise midway throughout ...


2

When upsampling the number video frames to allow playing a video in slow motion, rather than for a frame rate increase for smoothness, you will likely need to modify the audio using a time-pitch stretching/shifting algorithm to stretch the audio out (increase the number of samples) for a longer play duration (to match the increase in slow motion video ...


2

No. 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 ...


2

It seems to be at a stadium which uses 12 cameras and Replay-Technologies special signal processing called FreeD: https://www.youtube.com/watch?v=N1kgt2VDjdM The image you show seems to use 2-3 cameras, but the technology can work with multiple camera combinations from the 12 in 360'. It's a particularly stunning real-time effect, and there are also very ...


1

For me your question is not clear enough. This questions come to my mind: what exactly do you want to transmit? Do you want to transmit raw H.264 bitstream (NAL units) or maybe MPEG2-TS, or RTP packets? Most video transmission applications would use either MPEG2-TS or RTP. I suppose that you are not interested in HTTP or other TCP based streaming solutions. ...


1

The problem you want to solve is called automatic audio alignment, or auto-synchronization of audio. Common algorithms are phase correlation, cross correlation on the raw audio waveform. People also use more "cooked" audio features like MFCCs, say each 10ms frame, and measure frame-wise similarity between the audio segments to be compared. There are many ...


1

The ffmpeg implementation of the HEVC decoding follows this formula instead: $ |p_2,_0 - 2p_1,_0+p_0,_0|+|p_2,_3 - 2p_1,_3+p_0,_3|+|q_2,_0 - 2q_1,_0+q_0,_0|+|q_2,_3 - 2q_1,_3+q_0,_3|<\beta \ (1)$ This appears in the hevc_deblock.asm file. ;compare pcmpgtw m15, m13, m14 movmskps r13, m15 ;filtering mask 0d0 + 0d3 < beta0 (bit 2 ...


1

You can see for algorithms in ieee sites there are tons of algorithm which might suit your requirement and An Algorithm for Real-Time Eye-Movement Tracking with Free Head Movement Robust algorithm for video based eye tracing. Are some examples.


1

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. Gaussian ...


1

Implement a low pass 2D filter that will reduce greatly the noise. Following an example that you could complete yourself. You could subtract the filter bias to keep an average of 0 "addition" by the filter, controlling the dynamic range. Example of filter 1/16 1/8 1/4 1/8 1/16 1/16 1/8 1/4 1/2 1/4 1/8 1/16 1/16 1/8 1/4 ...


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