What will be the bitrate of 320x200 video at 25 fps, which is compressed by a videocodec with a compression ratio of 1: 150? Write with a formula.
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I have a traffic dump in csv file containing packet arrival time, frame length and boolean values for multiple flags.
Can someone please explain how to calculate packet size from the traffic dump.
I further want to generate distribution of the packet size in python.
I'm reading an avi file with approx 2MB size, 301 frames, 20 frames/sec (15 sec long video) and a size of 1024 * 1096 per frame.
When I'm reading the single frames with cv2 and resaving them in original size as png, then I'm getting a size of approx 600KB per picture/frame. So, I have in total 301 * 600KB = 181MB (original avi had 2MB).
Any idea why this is happening and how to reduce the file size of the single frames without changing the resolution? Idea is to somehow generate single frames from the original video, do detections with CNN and to resave the original video again with included detections and the output video shall be somehow very similar to input video (approx same file size, must not be avi format)
PNG files or single frames are in the most cases always larger than the original video file (compressed in the most cases by a codec https://www.fourcc.org/codecs.php). Use for example the following command on Linux to create a compressed avi:
ffmpeg -i FramePicName%d.png -vcodec libx264 -f avi aviFileName
You can get the used codec to create the original video file by the following python cv2 code
cap = cv2.VideoCapture(videoFile)
fourcc = cap.get(cv2.CAP_PROP_FOURCC) # or cv2.cv.CV_CAP_PROP_FOURCC
"".join([chr((int(fourcc) >> 8 * i) & 0xFF) for i in range(4)])
I'm using imagemagick to create an animated GIF out of ~60 JPG 640x427px photos. The combined size of the JPGs is about 4MB.
However, the output GIF is ~12MB. Is there a reason why the GIF is considerably bigger? Can I conceivably achieve a GIF size of ~4MB?
The command I'm using is:
convert -channel RGB # no improvement in size
-delay 2x10 \
-size 640 \
-loop 0 \
-dispose Background # no improvement in size
-layers Optimize # about 2MB improvement
portrait/*.jpg portrait.gif
Using gifsicle didn't seem to improve either.
JPG is lossy compression.
GIF is lossless compression.
A better comparison would be to convert all the source images to GIF first, then combine them..
First google hit for GIF compression is http://ezgif.com/optimize which claims lossy GIF compresion, might work for you but I offer no warranty as I haven't tried it.
JPEG achieves it's compression through a (lossy) transform, where an 16x16 / 8x8 block of pixels is transformed to frequency representation and then quantized. Instead of selecting e.g. 256 levels (i.e. 8 bits) of red/green/blue per component, JPEG can ignore some frequency components, or use just 1 or 2 bits to represent them.
GIF on the other hand works by identifying repeated patterns from a paletted image (upto 256 entries), which occur exactly in the previously encoded/decoded stream. Both because of the JPEG compression, and the source of the images typically encoded by JPEG (natural full color), the probability of (long) exact matches is quite low.
60 RGB images with the size 640x427 is about 16 million pixels. To represent that much in 4 MB, requires a compression of 2 bits per pixel. To achieve this with GIF would require a very lossy algorithm, that would select (vector) quantization of true color pixels not to the closest pixel in the target GIF palette, but based also on the fact how good dictionary of code words this particular selection will make. The dictionary builds slowly and to achieve 2 bits/pixel, the average length of the decoded code word would have to map to 5.5 matching pixels in the close neighborhood.
By contrast, imagemagick has been able to compress the 16 million pixels (each selected from a palette of 256 elements) to 75% already!
i'm using Omnivision ov5620
http://electronics123.net/amazon/datasheet/OV5620_CLCC_DS%20(1.3).pdf
this is datasheet.
than, you can see the Output Format 10-bit digital RGB Raw data.
first, i know RGB raw data is bayer array.
so, 10-bit RGB mean each channel of 1024 scale? range is 0~1023?
or 8-bit RGB each channel and four-LSB[2:0] is new fifth pixel data?
please refer the image
which is correct?
They pack every four adjacent 10-bit pixels (0..1023) of the line into 5 sequential bytes, where each of the first 4 bytes contains the MSB part of the pixel, and the 5th byte contains LSBs of all four pixels packed together into one byte.
This is convenient format because if you want to convert it to RGB8 you just ignore that fifth byte.
Also each displayed line begins with the packer header (PH) byte and terminates with the packer footer (PF) byte. And the whole frame begins with the frame start (FS) byte and terminates with the frame end (FE) byte.
I want to know how much data can be embedded into an image of different sizes.
For example in 30kb image file how much data can be stored without distortion of the image.
it depends on the image type , algoridum , if i take a example as a 24bitmap image to store ASCII character
To store a one ASCII Character = Number of Pixels / 8 (one ASCII = 8bits )
It depends on two points:
How much bits per pixel in your image.
How much bits you will embed in one pixel .
O.K lets suppose that your color model is RGB and each pixel = 8*3 bits (one byte for each color), and you want embed 3 bits in one pixel.
data that can be embedded into an image = (number of pixels * 3) bits
If you would use the LSB to hide your information this would give 30000Bits of available space to use. 3750 bytes.
As the LSB represents 1 or 0 into a byte that gets values from 0-256 this gives you in the worst case scenario that you are going to modify all the LSBs distortion of 1/256 that equals 0,4%.
In the statistical average scenario you would get 0,2% distortion.
So depends on which bit of the byte you are going to change.