dct

I am trying to implement JPEG compression using python. When I tried to apply the DCT, quantization, IDCT process for a tiff image, I found something strange for scipy.fftpack.dct/idct. Since there is only 1D dct/idct within scipy package, I was doing this for a 2D dct import numpy as np from scipy.fftpack import dct, idct def dct2(block): return dct(dct(block.T).T) def idct2(block): return idct(idct(block.T).T) I tested the 2D dct/idct using a simple 3x3 matrix. I was expecting to get a True matrix with this test case. a = np.random.randint(0,255,9).reshape(3,3) print a == idct2(dct2(a))

I went through how DCT (discrete cosine transform) is used in image and video compression standards. But why DCT only is preferred over other transforms like dft or dst? Because cos(0) is 1, the first (0th) coefficient of DCT-II is the mean of the values being transformed. This makes the first coefficient of each 8x8 block represent the average tone of its constituent pixels, which is obviously a good start. Subsequent coefficients add increasing levels of detail, starting with sweeping gradients and continuing into increasingly fiddly patterns, and it just so happens that the first few

I'm trying to understand the effect of the Block Size and best strategy of choosing the Coefficients in DCT compression. Basically I want to ask what I wrote here: Video Compression: What is discrete cosine transform? Lets assume the most primitive compression. Making block of an image. Performing a DCT on each blog and zeroing out some coefficients. To my understanding, the smaller the block the better. Smaller blocks means the Pixels are more correlated hence the energy in the DCT spectrum is more "Compact". It should be more emphasized in a fast varying images (High Frequency). Let's say we

Here I have my DCT algorithm class with "applyDCT" and "applyIDCT" methods. Technically after doing a forward DCT (discrete cosine transform) on a 2x2 table of random integers between 0 and 255, and then immediately doing a reverse DCT on these numbers, we should come back to the original integer numbers we had in the first place. In my case, this is not so. What am I doing wrong here? public class DCT { private static final int N = 2; private double[] c = new double[N]; public DCT() { this.initializeCoefficients(); } private void initializeCoefficients() { for (int i=1;i<N;i++) { c[i]=1; } c

I've implemented an image/video transformation technique called discrete cosine transform. This technique is used in MPEG video encoding. I based my algorithm on the ideas presented at the following URL: http://vsr.informatik.tu-chemnitz.de/~jan/MPEG/HTML/mpeg_tech.html Now I can transform an 8x8 section of a black and white image, such as: 0140 0124 0124 0132 0130 0139 0102 0088 0140 0123 0126 0132 0134 0134 0088 0117 0143 0126 0126 0133 0134 0138 0081 0082 0148 0126 0128 0136 0137 0134 0079 0130 0147 0128 0126 0137 0138 0145 0132 0144 0147 0131 0123 0138 0137 0140 0145 0137 0142 0135 0122