Effect of noise on speech compression in Run Length Encoding scheme

Background. In telecardiology, the bio-signal acquisition processing and communication for clinical purposes occupies larger storage and significant bandwidth over a communication channel. Electrocardiograph (ECG) compression with effective reproductivity is highly desired. In the present work, a compression technique for ECG signals with less distortion by using a non-decimated stationary wavelet with a run-length encoding scheme has been proposed. Method. In the present work non-decimated stationary wavelet transform (NSWT) method has been developed to compress the ECG signals. The signal is subdivided into N levels with different thresholding values. The wavelet coefficients having values larger than the threshold are evaluated and the remaining are suppressed. In the presented technique, the biorthogonal (bior) wavelet is employed as it improves the compression ratio as well percentage root means square ratio (PRD) when compared to the existing method and exhibits improved results. After pre-processing, the coefficients are subjected to the Savitzky-Golay filter to remove corrupted signals. The wavelet coefficients are then quantized using dead-zone quantization, which eliminates values that are close to zero. To encode these values, a run-length encoding (RLE) scheme is applied, resulting in compressed ECG signals. Results. The presented methodology has been evaluated on the MITDB arrhythmias database which contains 4800 ECG fragments from forty-eight clinical records. The proposed technique has achieved an average compression ratio of 33.12, PRD of 1.99, NPRD of 2.53, and QS of 16.57, making it a promising approach for various applications. Conclusion. The proposed technique exhibits a high compression ratio and reduces distortion compared to the existing method.

This paper presents a pipeline algorithm for MPI_Reduce that uses a Run Length Encoding(RLE) scheme to improve the global reduction of sparse floating-point data. The RLE scheme is directly incorporated into the reduction process and causes only low overheads in the worst case. The high throughput of the RLE scheme allows performance improvements when using high performance interconnects, too. Random sample data and sparse vector data from a parallel FEM application is used to demonstrate the performance of the new reduction algorithm for an HPC Cluster with InfiniBand interconnects.

This paper reports the effect of compression by applying delta encoding and Huffman coding schemes together on speech signals of American-English and Hindi from International Phonetic Alphabet database. First of all, these speech signals have been delta encoded and then compressed by Huffman coding. By doing so, it has been observed here that the Huffman coding gives high compression ratio for this delta encoded speech signals as compared to the compression on the input speech signals only incorporating Huffman coding.

Image compression plays a crucial role in optimising storage and transmission efficiency. This paper evaluates the performance of Run-Length Encoding (RLE), Huffman Coding, and Lempel-Ziv-Welch (LZW) algorithms for compressing grayscale PNG and JPG images. The study analyses their effectiveness using compression ratio, bits per pixel, and compression time as key performance metrics. Results indicate that LZW achieved the highest compression ratio, ranging from 1.0113 to 2.4020, making it the most efficient for file size reduction. RLE performed moderately, with compression ratios between 0.5456 and 2.3895, while Huffman Coding exhibited the lowest ratios, ranging from 0.2646 to 1.0680. In terms of bits per pixel, LZW recorded the lowest values, highlighting its ability to reduce data while preserving image quality. Compression time analysis revealed that RLE was the fastest, with processing times between 0.0019 and 0.0468 seconds, making it suitable for real-time applications. LZW and Huffman Coding demonstrated a trade-off between compression efficiency and speed. These findings establish LZW as the most effective algorithm for high compression with minimal quality loss, while RLE remains the best option for speed-critical applications.

Compression of speech signal is an essential field in signal processing. Speech compression is very important in today’s world, due to the limited bandwidth transmission and storage capacity. This paper explores a Contourlet transformation based methodology for the compression of the speech signal. In this methodology, the speech signal is analysed using Contourlet transformation coefficients with statistic methods as threshold values, such as Interquartile Filter (IQR), Average Absolute Deviation (AAD), Median Absolute Deviation (MAD) and standard deviation (STD), followed by the application of (Run length encoding) They are exploited for recording speech in different times (5, 30, and 120 seconds). A comparative study of performance of different transforms is made in terms of (Signal to Noise Ratio,Peak Signal to Noise Ratio,Normalized Cross-Correlation, Normalized Cross-Correlation) and the compression ratio (CR). The best stable result of implementing our algorithm for compressing speech is at level1 with AAD or MAD, adopting Matlab 2013a language.

This paper focuses on a pitch estimation method of noisy speech signal using the combination of empirical mode decomposition (EMD) and discrete Fourier transform (DFT). The noisy speech signal is filtered within the range of fundamental frequency. Normalized autocorrelation function (NACF) is computed from the pre-filtered noisy speech signal. The NACF is decomposed by EMD to generate a finite number of band limited signal called Intrinsic Mode Function (IMF). DFT is applied to NACF to determine the dominant frequency of the analyzing speech frame. The IMF with fundamental period closest to that of the dominant frequency is selected as the target IMF containing the fundamental period. The performance of the proposed pitch estimation method is compared in terms of gross pitch error (GPE) with the recent algorithms. The experimental results show that the proposed one performs better for noisy and clean speech signals.

In this paper we present a new lossless audio coding algorithm using Burrows-Wheeler Transform (BWT) and Run Length Encoding (RLE).Audio signals used are assumed to be of floating point values. The BWT is applied to the audio signals to get the transformed coefficients and then these resulting coefficients are better compressed using Run Length Encoding. Two entropy coding are used which are Run Length Encoding and Huffman coding. Proposed compression algorithm is experimented and analyzed for two different stereo type audio signals. Compression ratio and Bit rate for audio coding has been used as a comparison parameter for proposed audio coding algorithm. Experimental result shows that the lossless audio coding algorithm outperforms other lossless audio coding methods; using combined Burrows Wheeler Transform & Move to front coding method ,using combined Burrows Wheeler Transform and Huffman coding method, and using Burrows Wheeler Transform ,Move to front coding method & Run Length Encoding method.

Improving generative adversarial networks for speech enhancement through regularization of latent representations

In the medical industry, the amount of data that can be collected and kept is currently increasing. As a result, in order to handle these large amounts of data efficiently, compression methods must be re-examined while taking the algorithm complexity into account. An image processing strategy should be explored to eliminate the duplication image contents, so boosting the capability to retain or transport data in the best possible manner. Image Compression (IC) is a method of compressing images as they are being stored and processed. The information is preserved in a lossless image compression technique which allows for exact image reconstruction from compressed data with retain the quality of image to higher possible extend but it does not significantly decrease the size of the image. In this research work, the encoding algorithm is applied to various medical images such as brain image, dental x-ray image, hand x ray images, breast mammogram images and skin image can be used to minimize the bit size of the image pixels based on the different encoding algorithm such as Huffman, Lempel-Ziv-Welch (LZW) and Run Length Encoding (RLE) for effective compression and decompression without any quality loss to reconstruct the image. The image processing toolbox is used to apply the compression algorithms in MATLAB. To assess the compression efficiency of various medical images using different encoding techniques and performance indicators such as Compression Ratio (CR) and Compression Factor (CF). The LZW technique compresses binary images; however, it fails to generate a lossless image in this implementation. Huffman and RLE algorithms have a lower CR value, which means they compress data more efficiently than LZW, although RLE has a larger CF value than LZW and Huffman. When fewer CR and more CF are recorded, RLE coding becomes more viable. Finally, using state-of-the-art methodologies for the sample medical images, performance measures such as PSNR and MSE is retrieved and assessed.

Background/Objectives: This paper presents a color image compression method to increase the compression ratio without affecting the original scene by noise or distortion. Methods/Analysis: In this paper an attempt to minimize data loss without highly affect the compression ratio by employing low lossy rate quad-tree compression technique to increase the correlation between pixels that will enhance DCT results and also compress the data before DCT phase, using Quantization and entropy encoders such as run length encoding and shift encoding will further compress the image. Finding: For conducted six different visual information images, the Compression Ratio (CR) results of the proposed method on average to be around 1:29 of the size of the original image, more compression ratio can be achieved by increasing the compression levels, this high compression ratio is considered a best ratio compared with the achieved Peak Signal to Noise Ratio (PSNR) of the decompressed-compressed image. Application/Improvements: This compression system can be used in Storing or Transforming Color Images due to its high compression ratio while the noise and distortion still as minimum as possible.

In this work, a new noise robust and variable length frame based speech modeling method is introduced. This method consists of three major steps which includes noise removal algorithm, coding and encoding algorithms, respectively. Coding and encoding parts are developed based on SYMPES (SYsteMatic Procedure for Predefined Envelope and Signature sequence sets). These sets have been developed in two types which represent voiced and unvoiced parts of the speech signals separately in order to obtain more efficient coding strategy and higher compression ratio while preserving the perceptual quality of the speech signals. As an extension of our previous works our new framework is not only consider the coding of the clean speech signals but also noisy speech signals. The new noise robust module suppresses the noise and delivers the clean speech signal to the newly designed modeling part. The modeling part promises higher compression ratios by switching to the more appropriate type of predefined sets take into account the voiced and unvoiced frames.

In this paper, We implement the Discrete Cosine Transform (DCT) coding (lossy compression method) followed by the proposed coding technique which called “Quantized Huffman Coding” in order to minimize the EEG data size. Therefore, adding a lossless compression algorithm after the lossy compression is a good idea to get a high compression ratio with acceptable distortion in the original signal. Here, we use DCT encoder followed by either quantized Huffman Coding or Run Length Encoding (RLE) then compare between them. Our work shows that, at the same Root Mean Square Error (RMSE), the quantized Huffman coding outperforms the RLE in some aspects as Compression Ratio (CR) and time consumed in compression and decompression, but Structural Similarity Index (SSIM) is the same for the two techniques.

This paper proposes an image processing approach for compression of ECG signals based on 2D compression standards. This will explore both inter-beat and intra-beat redundancies that exist in the ECG signal leading to higher compression ratio (CR) as compared to 1D signal compression standards which explore only the inter-beat redundancies. The proposed method is twofold: In the first step, ECG signal is preprocessed and QRS detection is used to detect the peaks. In the second step, baseline wander is removed and a 2D array of data is obtained through the cut-and-align beat approach. Further beat reordering is done to arrange the ECG array depending upon the similarities available in the adjacent beats. Then ECG signal is compressed by first applying the lossless compression scheme called the 2D Run Length Encoding (RLE), and then a variant of discrete wavelet transform (DWT) called set partitioning in hierarchical trees (SPIHT) is applied to further compress the ECG signal. The proposed method is evaluated on the selected data from MITs Beth Israel Hospital, and it was conceded that this method surpasses some of the prevailing methods in the literature by attaining a higher compression ratio (CR) and moderate percentage-root-mean-square difference (PRD).

In this paper, We propose a hybrid compression technique by integrating the Discrete Cosine Transform (DCT) and a Non-Uniform Quantized Huffman in order to minimize the Electroencephalography (EEG) data size. Therefore, to get a high compression ratio we apply the lossy compression followed by a lossless compression algorithm. We use DCT encoder followed by either non-uniform quantized Huffman (NonUQH) coding or Run Length Encoding (RLE) then compare between them. The system performance is evaluated in terms of the compression/decompression time, the compression ratio, and the root mean square error. The proposed hybrid technique DCT/NonUQH achieves 90% compression compared to 59% by DCT/RLE with the same similarity. Furthermore, it needs 50% less time for compression/decompression process.

Some error-free and irreversible two-dimensional direct-cosine-transform (2D-DCT) coding, image-compression techniques applied to radiological images are discussed in this paper. Run-length coding and Huffman coding are described, and examples are given for error-free image compression. In the case of irreversible 2D-DCT coding, the block-quantization technique and the full-frame bit-allocation (FFBA) technique are described. Error-free image compression can achieve a compression ratio from 2:1 to 3:1, whereas the irreversible 2D-DCT coding compression technique can, in general, achieve a much higher acceptable compression ratio. The currently available block-quantization hardware may lead to visible block artifacts at certain compression ratios, but FFBA may be employed with the same or higher compression ratios without generating such artifacts. An even higher compression ratio can be achieved if the image is compressed by using first FFBA and then Huffman coding. The disadvantages of FFBA are that it is sensitive to sharp edges and no hardware is available. This paper also describes the design of the FFBA technique.