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Dithering and Quantization of Audio and Image

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Dithering and Quantization of Audio and Image Dithering and Quantization of audio and image Maciej Lipiński - Ext 06135 1. Introduction This project is going to focus on issue of dithering. The main aim of assignment was to develop a program to quantize images and audio signals, which should add noise and to measure mean square errors, comparing the quality of the quantized images with and without noise. The program realizes fallowing: - quantize an image or audio signal using n levels (defined by the user); - measure the MSE (Mean Square Error) between the original and the quantized signals; - add uniform noise in [-d/2,d/2], where d is the quantization step size, using n levels; - quantify the signal (image or audio) after adding the noise, using n levels (user defined); - measure the MSE by comparing the noise-quantized signal with the original; - compare results. The program shows graphic result, presenting original image/audio, quantized image/audio and quantized with dither image/audio. It calculates and displays the values of MSE – mean square error. 2. DITHERING Dither is a form of noise, “erroneous” signal or data which is intentionally added to sample data for the purpose of minimizing quantization error. It is utilized in many different fields where digital processing is used, such as digital audio and images. The quantization and re-quantization of digital data yields error. If that error is repeating and correlated to the signal, the error that results is repeating. In some fields, especially where the receptor is sensitive to such artifacts, cyclical errors yield undesirable artifacts. In these fields dither is helpful to result in less determinable distortions. The field of audio is a primary example of this — the human ear hears individual frequencies. The ear is therefore very sensitive to distortion. When we dither, we add very low level random noise to signal in order to mask the imperfections of digital audio. Covering at all frequencies is far less sensitive and increases the perceived dynamic range. Basically the audio dithering is commonly used when we want to convert for instance from 24-bit to 16- bit. We are cutting off the last 8 bits of information from each sample of audio, which correlates to very low level sounds in the mix. Without dithering we would lose that information, and also have errors, which sound as added harshness and noise (because the signal is random, the average signal is 0. Information that is contained in the lowest 8 bits of 24-bit audio actually modulates the random signal, so that the average is equal to the audio from the lowest 8 bits). On the other hand, dithering images is a technique used in graphics to create the illusion of color depth. In a dithered image, colors not available in the palette are approximated by a combination of colored pixels from within the available palette. The human eye perceives the diffusion as a mixture of the colors within it . A simple example is an image with only black and white in the color palette. By combining black and white pixels in complex patterns can create the illusion of gray values. Such a example is shown and described in the further part. 3. Work Environment In order to allow processing either images or audio signals the program has two interfaces. The main window lets choose which part user wants to consider : The interface, which is connected with performing of images looks following : This part of program allows to execute quantization of image with specific quantization level “N”, which is set by user in the left field on the top. The correct values are from the range <1,8>. In the case of the number out of range, program will show error communication. By the button in the middle of the top the user can choose image, which is going to be processed. Loaded file appears below the button with uppercase heading. It is described by two dimension parameters in pixels “width” and “height”. After selecting image and setting the quantization level it is time to processed, by the “Processing...”. As a result of that, in the window will emarge two images, at first quantized , and as the second one the quantized with added noise – dithered. Below each of appropriate image it will be calculated the MSE. The second opportunity is to work on audio signals, so in this connection user may choose antoher interface shown below : Like previosly the user has possibility to choose the audio file to work with. After selecting, on the interface screen will come out the original signal and the button to listen to file, which can be heard from the loudspeakers. What is more that on the field “Audio file properties” the user will get the basics parameters of the signal such as : - Number of samples - Sampling frequency - Bits on sample Comparing to image processing, the user may set the quatization level either. Additionaly it is option to change form of noise – dither which is going to be added to original signal. The user can choose between : - White Noise - Pink Noise “Processing” causes that below the adequate headlines will show another buttons, which previosly are hidden, for listen and view the obtain modified signals. As well the user will get calculated values of MSE for both examples. Pushing the “show” as a result the user will see the new window with two plots , the first quantized and second quantized with added dithering: 4. Algorithm Quantization is the process of approximating a continuous range of values by a small set of discrete symbols or integer values, described in the program by “N” quantization level, where 2 N is the set of possible values (in the case of images). Distance between possible values is associated with variable “step”: Step = 256/ N, where N is number from <1,8> . Either original audio/image or quantized and quantized with dither audios/images are kept as a matrices. The sizes of matrices are the same. In this way quantization of the original image/audio is the effect of reduction values of the matrix of original to the range of values set indirectly by “N”and “step” : q_img=floor(image./step).*step+step/2 The noise is added to original image/audio by selecting random number from the size of matrix(image/audio) as following : n = (rand(size(image))-0.5).*step; img_d = image + n; The Mean Square Error, which is squared difference between original and processed samples is simply counted as : error=(q_img-image).^2 In the case of audio processing the amplitude of the signal is in the range <-1,1>, so “step” is described on the other way : step=2/(Nlevel) , where “Nlevel” is the value set by the user in the field of interface. The user has possiblity to choose the type of noise is going to be added. The uniform noise is covered on original signal in the range [-step/2, step/2]. The part of code is the same like in previouse example. 5. Results ( Image processing) The main aim was to focus on one part - audio or image, compare the results and draw a conclusions. The experiments were made mostly for the images. The simulations were done with different kinds of images (jpg, tiff, bmp), different sizes, with different backgrounds, and for the different quantization levels. The results of calculated MSE’s are collected in a table below : Image “N” Mean Square Error Size quantization level Quantized Image Quantized with dither 3 905,76 1150,36 xray.bmp 5 310,606 421,679 256×256 7 155,37 218,816 3 510,255 1077,06 lena.tif 5 264,921 475,021 512×512 7 104,113 218,08 3 607,601 1159,44 boats.tif 5 249,593 454,681 640×640 7 105,674 212,127 3 488,858 897,732 frame.tif 5 225,056 410,695 352×240 7 101,704 206,585 3 871,221 1197,46 connie_nielsen.jpg 5 312,273 450,398 348×200 7 156,769 230,061 3 852,843 1124,47 Berries.jpg 5 298,797 426,742 348×277 7 151,521 221,2 3 605,871 1036,26 Abstract.jpg 5 214,249 391,663 640×480 7 109,797 206,954 3 666,446 1018,58 mountain.jpg 5 225,595 381,829 800×600 7 119,371 206,734 3 643,161 961,988 porshe.jpg 5 247,759 393,375 1024×768 7 121,905 201,744 3 652,406 1078,78 tree.jpg 5 244,501 420,464 1024×786 7 120,453 214,589 The table does not present subjective inforamtions. From the obiective point of view it can be noticed that - for the images with little contrast the smaller size, the bigger MSE of quantized image - for all kind of images the results for quantization with dither are relatively close - MSE is much higher in the images with added dither Now it is worth to verify and look deeper on some of results. The presented images are quantized with 5 level. From the top is going to be shown original image, below from the left side quantized image and quantized with added dither image. The first two are grayscale images. On the second page are presented the results. As we can see quantizaton is the process which “cut off” available values from the original palette to limited palette . For grayscale images the effect of quantization is very visible. boats.tif xray.bmp xray.bmp In the both examples we can definatly affirm that the quantization with added dither is quite better than just the quantization.
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