# Tag Info

65

The color burst is also an indicator that there is a color signal. This is for compatibility with black and white signals. No color burst means B&W signal, so only decode the luminance signal (no croma). No signal, no color burst, so the decoder falls back to B&W mode. Same idea goes to FM stereo/mono. If there is no 19 kHz subcarrier present, ...

26

In the absence of a valid color burst signal, the "color killer" circuit disables the color difference signals, otherwise you would indeed see colored noise. This is mainly intended for displaying weak signals in B/W without the colored noise. One step further is to mute the entire signal, substitute stable sync signals, and display a blue or black field ...

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.

3

QAM is a digital modulation scheme. As such it is one way of implementing a physical layer that allows to convey digital information over a given medium. QAM is frequently used in all kinds of systems, including wireless (cf. broadcast TV and yes, also WiFi) as well as wired (Ethernet uses some variations of QAM as well). What kind of information you convey ...

3

Band-pass filtering with cut-off frequencies of 300 Hz and 3400 Hz should result in a good approximation. Try with a Chebychev filter or order not more than 6. Then you may need to downsample your audio to 8000 samples per second, which is the standard for telephony. P.S. The actual cut-off frequencies (especially the 3400 Hz) may be different according to ...

3

One straightforward approach is to do blur detection (see here and here) and prune the frames that are detected.

3

In PAL, the colour information (chrominance or chroma) is modulated onto the black and white (luminance or luma) baseband signal. The chroma is at ~4.4MHz offset from DC and is about 1.3 MHz wide. Assuming that your noise is centered around DC then, if it is less than ~3.5MHZ wide then it won't appear in the chroma spectrum and will only be in the luma. ...

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

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 ...

2

Extracting Audio from Video There is a simple cross-platform tool for manipulating / accessing video & audio files called ffmpeg. I've linked you the documentation available for the whole toolkit, just so you can glance around and get a feel for what sort of wonders can be performed with it. But, all you need to do is extract audio from your video file....

2

To add to the existing answers, PAL corrects for color errors by reversing the color component of each line in the next one, which cancels out color errors and should also reduce the color component of random noise. While this effectively halves the color resolution, it has more lines to start with than NTSC and the eye doesn't have high of a color ...

2

I did this once using two phones and by making and actual call. The calling phone had a good quality headset pushed against its microphone, and the audio was played through the headset. The audio was recorded from the headphone output connector of the receiving phone. The drawback is that the following things may be hard to replicate if someone wants to ...

2

When one does an operation Wrong / Right aren't strictly defined. In most cases the questions are: What's the model? Is the model reasonable? When you do sub pixel motion estimation on frames which are highly correlated you can assumes the change in values, even after non linear operation like Gamma Function, is small. So basically the model assumes ...

1

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 – ...

1

The vision centre of the brain retains an image for $\frac{1}{15}^{th}$ of a second and any image added in that time frame gives a sense of continuity. 24 FPS is mostly used in movies and TV. Also, the images do not move horizontally or vertically in spatial domain to create a sense of motion.

1

In this case, images/frames are moving horizontally or vertically? Neither. They just appear; usually, they stand still for as long as a frame is displayed. But what is the minimum rate of frame/images so that still images appear as video? That depends on a lot of factors like brightness, amount of motion in the material, and mental state of the ...

1

I am not an Octave user, but the Video package appears to do these things. Good luck!

1

A good answer might constitute a few chapters in a good DSP text, making this question overly broad. So I'll sketch an over-simplified skeleton, where you will need to seek a decent rationale elsewhere. Pretend your captured signal is just a vector (array of samples). Move your signal of interest "down" to "baseband" (resulting in either 2 vectors (X,Y) ...

1

It is a matter of "getting away with it". The multi-tap algorithm is not doing a perfect job either because it's not a brick-wall sinc filter, giving some error even for linear color space input. Also the original data may not be perfectly sampled, which can be considered a further source of error. Small error statistics are usually approximately additive. ...

1

First of all, you mistakenly assume that those multimedia source files are sent as raw bits (or bytes); no but instead in compressed form. Hence you shall not compute the raw bitrates of those file types unless you really want to send the raw bits associated with an uncompressed PCM audio file or RGB-BMP image-video file. The actual bits which are sent come ...

1

Your calculations are wrong. You have misunderstood the concept. The files may contain an amount of bits as you calculated but transferring that information is a bit different. First of all, you send information as PACKETS. Here is an example packet type which is being used in Bluetooth. The only place you send the actual information is called PAYLOAD. As ...

1

If you know where the logo is then you can detect its presence, fairly quickly, with cross-correlation...with a little bit more work. Detecting the presence of "things" using cross-correlation is a way to do template matching, where we are trying to find a specific pattern in a longer "duration" signal. In this case, the logo is the pattern and the longer "...

1

It does not determine the framerate, but it has an influence on the framerate. The first caveat is that although you're only waiting 1ms between frames, the program has other work to do in order to render each frame, so the delay between frames is not purely caused by that 1ms wait. The next caveat is that (from the doc) Since the OS has a minimum time ...

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In video, pixels are not usually encoded independently; you will need to decode full frames. If you are going to run several experiments on a single video, I would suggest that you decode the video once frame-by-frame and write the border pixels into a file in your own raw format for easy access. This should give a manageable file size perhaps of a few ...

1

RTP Payload Format for H.264 Video 0 1 2 3 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ |V=2|P|X| CC |M| PT | sequence number | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+...

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I hope I'm understanding you correctly: What you want to do is hide data in the encrypted version of a video? That will break everything. Simple as that. A very important aspect to good encryption is that a small change in the cleartext data (your unencrypted video) will result in a completely changed encrypted video; the same applies in the other ...

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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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Extracting one single hash per video and expecting it to work for subsegment matching is unrealistic. The approach - which is the one used in audio identification systems but could be extended to video - is to extract many hashes, one for each small segment of content (say a few seconds of video). The matching process consists in matching your "query" ...

1

yes - a video signal would be considered 3rd order tensor. scalars are 0-order tensors, vectors are 1st order tensors, matrices are 2nd order tensor, data volumes are 3rd order tensors and so on for multi-dimensional arrays.

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