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3

Below shows design considerations for the filter design and you can use common tools in Matlab/Octave and Python Scipy.Signal to determine the filter coefficients (impulse response) using this criteria. (such as the firls and firpm filter design commands in Matlab). When you insert zeros, you create replicas in frequency such as I show in the diagram below, ...


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The dot in that summation is just scalar multiplication. And yes, it's a convolution -- you're convolving the input signal by the filter.


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JPEG compression relies on a number of techniques while reducing an image's storage size. Primarily it's the DCT stage which accounts for the gross bit reduction. This stage is controlled by the quality parameter. However, color is also used to advantage as follows. It's experimentally verified that our eyes are more sensitive to brightness resolution than ...


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You seem to be confused between the difference between what you want to do, the standards that exist for doing it with video, and tools that you might use to do it with -- apparently -- still images. What you want to do You want to separate out the chrominance channel, then you want to average it in 10x10 blocks (for a factor of 100), then you want to make ...


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For demonstration purposes GIMP is just fine. It can split an image to three separate YUV/YCbCr images. You can then manually resize the UV/CbCr images by any amount to downsample the chroma. Then upsample back to original resolution and recombine the image again.


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I'd consider why you really want to do this - I personally can't think of a reason why I'd want to downsample to a specific sample number but I don't know your project Floating an alternate idea, you could downsample until you're near around that level of decimation and then truncate? It won't be 100 samples exactly but it might be easier in the long run to ...


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First, all of these routines act on an input array. Your comment "values beyond the boundary of the signal are NOT zeros" implies that you want to process a continuous signal, or at least one that is longer than a single call and array. If you want to use these routines, you’ll need some buffer management of your signal. Second, for converting 611 to 100, ...


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I will explain why method 2 is often a better choice over method 3. The frequency domain approach is equivalent to the "Windowing" method of filter design- in that to do that approach correctly you should window your data before taking the FFT. For an anti-alias filter design in the time domain approach, the least squares filter design algorithm outperforms ...


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The recommended way of converting an oversampled bandlimited signal into its critical sampling rate (or somehow above that) is to use a time-domain LP filter and decimate approach. This can be efficiently implemented using a polyphase filterbank architecture as well. The lowpass filter can be implemented using DFT/FFT frequency domain techniques if the ...


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must maintain a specific FFT length and small FFT bin width, down at baseband. Well, the FFT bin width is defined as $\frac{f_\text{sample}}{N}$, with $N$ being the FFT length and $f_\text{sample}$ the sampling rate of the signal that undergoes the FFT. So, if both these parameters are fixed, you have no freedom in choosing the bin width whatsoever. Note ...


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