So, the output will contain only integer values grouped in the upper left corner of the output matrix. An example of an output compressed frame block is depicted in Figure 3. The final step of the compression process is the coding stage. In this stage, the output matrix of the quantization step is converted into a binary data stream. The JPEG technique encodes the quantized elements by arranging them into a zigzag sequence. Arranging the quantized elements will facilitate the encoding by putting the non-zero values first before the zero values.
Once the signal is compressed and coded, it will be ready for transmitting or storing. However, when receiving such data or in the case of retrieving , the system needs to reconstruct the source signal. This is done by the decompression process. Decompression is the activity of restoring the source data from the compressed counterpart. This process could change some values of the original data depending on the compression rate used in the system. Regardless of the compression rate that was used, the following equation is used to reconstruct of the spectrum of the original speech signal:.
Equation 6 is used to generate the speech signal recovery from the reconstructed signal:. According to Eq. In other words, if there is proper selection of the quantization matrix, fine recovering values will be obtained. Two strategies were experimented to test the proposed method.
The first one is to modify the JPEG parameters. This includes the parameters of Eq. Modifying the parameters in Eq. Moreover, choosing the right quantization parameter values is very hard as there is no quantization vector generated that depends on the nature of the speech signal. The second strategy, which is adopted in this work, is to convert the speech signal from a 1D vector into an image-like 2D matrix.
This includes the essential preparations that are required to make the speech data values more appropriate to work with the JPEG algorithm, especially in terms of negative values. In order to examine the proposed method, the speech signal is divided into a fixed length of 5-ms duration that represents 64 samples.
This process is applied for the whole speech signal. Some of these frames have no or little information about the speech, like silence or just noise. So, one important role in the signal preparation is to remove the silence frames with low energy from the speech, as they have little effect on the speech signal. The selected frames are collected in one matrix to generate one image-like speech matrix.
The compression process will apply to the matrix of the signal so that a set of parameters will be generated. These will represent the key parameters in the decompression process. Figures 4 — 7 show examples of the original and decompressed signal frames with different compression rates. The behavior of the compressed and decompressed signal reveals two crucial views about the speech signal compression techniques.
First, the reconstructed signal is highly affected by the energy of the compressing frame. This, to some extent, can be justified by the fact that a tiny change in low-energy signals could cause a noticeable change in the signal. Figure 4 shows two different signal frames of the same speech signal with different energies, both compressed using the Q 10 quality matrix.
The differences between the original frame signal and the reconstructed one are clear to notice. Figure 4A shows that the reconstructed signal is highly distorted. This is because the energy of the frame signal is low quite silent speech frame. Figure 4B shows that the reconstructed signal is quite identical to the original one.
Similar cases are depicted in Figures 5 — 7. This will prove that the signal energy has a huge effect on the compressing quality regardless of the method or parameters that are used for signal compression. Second, the proper selection of the quantization matrices can minimize the differences between the two signals. This is clearly the case in Figures 4 — 7. The reconstructed signal is the best using Q 90 Figure 7 , and it is less with other quality matrices Figures 4 and 5.
Therefore, in order to get the best match between the reconstructed signal and the original one, the quality matrix as well as the signal energy should be focused on. The error distortion of the low-energy frames could happen because these frames can hold little information about the word being said; in other words, noise-like signals can suffer more distortion than the real informative speech signals. The high-quality matrix has a good less distorted reconstructed signal but, at the same time, less compression rate in terms of file size.
The low-quality matrix could cause more distortion or even wipe off in the reconstructed signal. So, the chosen quality matrix should compromise between the quality and size. Comparing the methods presented in the literature review, some important points about the proposed model can be seen.
First, the model suggests that the perceptual quality of the images can be used in speech signal processing compression in this model. This proves that the quantization matrices suggested for the image compression technique can be adopted in the speech signal processing in terms of compression or encoding. Second, in terms of the accuracy, the proposed method gives a low accurate similarity between the compressed and reconstructed signals, especially with low-level quantization matrices and low-energy signal noise-like.
In order to improve the result quality, some systems suggest using a combination of many techniques on one model [ 7 , 13 , 17 ]. This, however, will add some complexity to the system and can increase the required time for the system. No more parameters are needed to improve the system performance. Third, in the case of the signal being buried under an outside noise, the compression process needs to be preceded by a filtering step de-noising. This subject is out the scope in this paper.
However, many filter types can be adopted for this purpose but a filter that is highly accurate in denoising will be preferred. This paper has introduced a new compression strategy that explores the potential characteristics of the JPEG method to compress the speech signal. The comparison results have demonstrated the system robustness in reconstructing the speech signal with little change especially in the case of the low-energy parts of the signal.
Although the system is highly accurate when using the quality level matrix Q 90 , the problem with the low-energy frame makes it a bit far from ideal reconstructing.
So, a new set of quality matrices or new strategy is needed to solve the low-energy part of the speech signal. The main contribution of this research is modifying a 1D signal speech in a way that makes it appropriate with a 2D compression algorithm like JPEG. The modification includes two stages: first, the speech signal is brroken down into fixed-length frames and arranging the accepted ones depending on their energies in 2D form. Second, the speech signal data usually involve both the positive and negative values.
This is absolutely not accepted by the JPEG method, so the system does some steps to overcome this problem by increasing the base value of the speech signal data that guarantees that all speech data are converted into the positive values. The increment parameter will vary depending on the speech sample on hand. In general, the proper compression rate will highly rely on two major factors: the first, is the energy of the speech signal; the higher the signal energy is, the best results will be achieved.
Second, the higher the quality of the matrix applied on the signal is, the less is the produced distorted signal. Aloui, S. Bousselmi and A. Cherif, DSP real-time implementation of an audio compression algorithm by using the fast Hartley transform, Int. Search in Google Scholar. Bouguezel, M. Ahmad and M. Cabeen and P. Gent, Image compression and the discrete cosine transform , Math 45, College of the Redwoods, Fgee, W.
Phillips and W. Robertson, Comparing audio compression using wavelets with other audio compression schemes, in: Engineering Solutions for the Next Millennium. Haines and S. Chuang, The effects of video compression on acceptability of images for monitoring life sciences experiments, NASA technical paper. Hussain, D. Al-Jumeily, N. Radi and P. Lisboa, Hybrid neural network predictive-wavelet image compression system, Neurocomputing , — Khobragade and S.
Kornblum, Using jpeg quantization tables to identify imagery processed by software, Digit. Madane, Z. Shah, R. Shah and S. It does have a lossy mode, which is almost never used because it has a known issue.
It has an awkward way of introducing compression artifacts that substitute symbols — and as no-one wants their order for 8.
With the right predictor, this closely resembles the algorithm used by PNG. Unfortunately not compatible, but almost equal in performance. There are a lot of programs that promise to compress PDF files and make them smaller, but of all the ones I tried, the only one I would personally endorse is… actually mine. I wrote it. Thanks for this article. I just want to know if we have lossless algorithm to compress PDF files?
Excellent article! Your email address will not be published. Flate compression Flate or deflate as it is sometimes called is a rather complex compression algorithm. From Acrobat Distiller 4 onwards, there are 5 different levels of compression: Minimum, with a quality loss that will be acceptable for everything but the most demanding jobs. There are a number of Acrobat plug-ins that can recompress data.
I particularly like Quite-a-box-of-tricks from Quite software but there are others available. You should really take a look at the JBIG2 format. It is amazing. Leave a Reply Cancel reply Your email address will not be published. A cookie is used to collect visitor statistics.
The ads also use one. This is to certify that the project titled " Speech Compression using Wavelets " is a bona fide record of the project work done by Name University No. This is to certify that the project titled " … Expand. Speech Compression Using Wavelets. In the recent years, large scale information transfer by remote computing and the development of massive storage and retrieval systems have witnessed a tremendous growth.
To cope up with the growth … Expand. Discrete wavelet transform techniques in speech processing. The trend towards real-time, low-bit-rate speech coders dictates current research efforts in speech compression. A method being evaluated uses wavelets for speech analysis and synthesis. This paper studies the effect of different compression constraints and schemes presented in a new and flexible paradigm to achieve high compression ratios and acceptable signal to noise ratios of … Expand.
Wavelets and signal processing. Computer Science, Mathematics. A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and tutorial purposes.
The discussion includes nonstationary signal analysis, scale versus frequency, wavelet … Expand. International Journal for Applied Sciences,. Related Papers. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy , Terms of Service , and Dataset License.
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