A study of data coding technology developments in the 1980-1985 time frame, volume 2

The source parameters of digitized analog data are discussed. Different data compression schemes are outlined and analysis of their implementation are presented. Finally, bandwidth compression techniques are given for video signals

Data compression techniques applied to high resolution high frame rate video technology

An investigation is presented of video data compression applied to microgravity space experiments using High Resolution High Frame Rate Video Technology (HHVT). An extensive survey of methods of video data compression, described in the open literature, was conducted. The survey examines compression methods employing digital computing. The results of the survey are presented. They include a description of each method and assessment of image degradation and video data parameters. An assessment is made of present and near term future technology for implementation of video data compression in high speed imaging system. Results of the assessment are discussed and summarized. The results of a study of a baseline HHVT video system, and approaches for implementation of video data compression, are presented. Case studies of three microgravity experiments are presented and specific compression techniques and implementations are recommended

Adaptive Speech Compression Based on Discrete Wave Atoms Transform

This paper proposes a new adaptive speech compression system based on discrete wave atoms transform. First, the signal is decomposed on wave atoms, then wave atom coefficients are truncated using a new adaptive thresholding which depends on the SNR estimation. The thresholded coefficients are quantized using Max Lloyd scalar quantizer. Besides, they are encoded using zero run length encoding followed by Huffman coding. Numerous simulations are performed to prove the robustness of our approach. The results of current work are compared with wavelet based compression by using objective criteria, namely CR, SNR, PSNR and NRMSE. This study shows that the wave atoms transform is more appropriate than wavelets transform since it offers a higher compression ratio and a better speech quality

A comparative study of image compress schemes

Image compression is an important and active area of signal processing. All popular image compression techniques consist of three stages: Image transformation, quantization (lossy compression only), and lossless coding (of quantized transform coefficients). This thesis deals with a comparative study of several lossy image compression techniques. First, it reviews the well-known techniques of each stage. Starting with the first stage, the techniques of orthogonal block transformation and subband transform are described in detail. Then the quantization stage is described, followed by a brief review of the techniques for the third stage, lossless coding. Then these different image compression techniques are simulated and their rate-distortion performance are compared with each other. The results show that two-band multiplierless PR-QMF bank based subband image codec outperforms other filter banks considered in this thesis. It is also shown that uniform quantizers with a dead-zone perform best. Also, the multiplierless PR-QMF bank outperforms the DCT based on uniform quantization, but underperforms the DCT based on uniform quantization with a dead-zone

Audio Compression Using DCT and DWT Techniques

In today’s world multimedia files are used, storage space required for these files is more and sound files have no option so ultimate solution for this is compression. Compression is nothing but high input stream of data converted into smaller size. Speech Compression is a field of digital signal processing that focuses on reducing bit-rate of speech signals to enhance transmission speed and storage requirement of fast developing multimedia. In many applications, such as the design of multimedia workstations and high quality audio transmission and storage, the goal is to achieve transparent coding of audio and speech signals at the lowest possible data rates. Therefore, the transmission and storage of information becomes costly. However, if we can use less data, both transmission and storage become cheaper. Further reduction in bit rate is an attractive proposition in applications like remote broadcast lines, studio links, satellite transmission of high quality audio and voice over internet. This paper explores a transform based methodology for compression of the speech signal. In this methodology, different transforms such as Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT) are exploited. A comparative study of performance of different transforms is made in terms of Signal-to-noise ratio (SNR) and  Peak signal-to-noise ratio (PSNR). The mean compression ratio is also calculated for all the methods and compared. The simulation results included illustrate the effectiveness of these transforms in the field of data compression. Keywords-DCT (Discrete cosine transform), DWT (Discrete wavelet transform), Quantization Compression Factor (CF), Signal to Noise ratio (SNR)