Bit allocation algorithm for wavelet image compression

This paper presents solutions to the entropy-constrained scalar quantizer (ECSQ) design problem for two sources commonly encountered in image and speech compression applications: sources having the exponential and Laplacian probability density functions. We use the memoryless property of the exponential distribution to develop a new noniterative algorithm for obtaining the optimal quantizer design. We show how to obtain the optimal ECSQ either with or without an additional constraint on the number of levels in the quantizer. In contrast to prior methods, which require a multidimensional iterative solution of a large number of nonlinear equations, the new method needs only a single sequence of solutions to one-dimensional nonlinear equations (in some Laplacian cases, one additional two-dimensional solution is needed). As a result, the new method is orders of magnitude faster than prior ones. We show that as the constraint on the number of levels in the quantizer is relaxed, the optimal ECSQ becomes a uniform threshold quantizer (UTQ) for exponential, but not for Laplacian sources. We then further examine the performance of the UTQ and optimal ECSQ, and also investigate some interesting alternatives to the UTQ, including a uniform-reconstruction quantizer (URQ) and a constant dead-zone ratio quantizer (CDZRQ).

Image Compression plays a critical role in several important and diverse applications and has motivated the need for better image compression algorithms. Wavelets provide a powerful method for image compression; wavelet transform and wavelet packet transform have proved to be efficient and useful tools for image processing in the recent years. This paper aims at comparing different parameters of some well known wavelets at various threshold values concerning the quality of the reconstructed image. This paper attempts to compare compression performance using wavelet transform and wavelet packet transform for level 5 decomposition and highlights most desirable characteristics of wavelets.

Focused on the image data storage, compression and transmission of mars rover, system design and key hardware/software architecture design are carried out in this paper based on the analysis of function requirements and key design features. Image compression algorithm and transmission protocol are proposed, and the image storage and compression module in Mars rover is designed and implemented. The multi-camera partition parallel image file system based on FPGA（Field Programmable Gate Array）is realized. The adaptive first 1 run length coding algorithm for image compression based on DSP is realized on this module. Finally the function of multi-camera image data storage and management, flexible control of image compression ratio, quality progressive transmission, coding of region of interest, anti-error code diffusion and image thumbnail downward transmission are realized in the engineering development, meeting the reliable, efficient and flexible application requirements of China’s first Mars exploration mission.

The objective of this paper is to implement SPIHT and WDR image coder. We have used here two different images Natural and Artificial. Artificial image stand for image on which enhancement techniques have been applied e.g Wallpaper whereas Natural image is an image on which no enhancement technique has been applied e.g image captured by camera directly. SPIHT stands for Set Partitioning in Hierarchical Trees (SPIHT) Coding. The SPIHT coder is a highly refined version of the EZW algorithm and is a powerful image compression algorithm that produces an embedded bit stream from which the best reconstructed images in the mean square error sense can be extracted at various bit rates. Some of the best results -- highest PSNR values for given compression ratios for a wide variety of images have been obtained with SPIHT. Hence, it has become the benchmark state-of-the-art algorithm for image compression. One of the defects of SPIHT is that it only implicitly locates the position of significant coefficients. This makes it difficult to perform operations which depend on the position of significant transform values, such as region selection on compressed data. Region selection, also known as region of interest (ROI), means a portion of a compressed image that requires increased resolution. Such compressed data operations are possible with the WDR algorithm of Tian and Wells. The term difference reduction refers to the way in which WDR encodes the locations of significant wavelet transform values. Although WDR will not produce higher PSNR values than SPIHT. With the implementation of these algorithms we have calculated the various parameters e.g. CR, PSNR, BPP & MSE to analyze PQS (picture Quality Scale). We have analyzed our results using various wavelet filters such as Biorthogonal, Coif lets, Daubechies and Symlets etc.

In distributed underwater signal processing for area surveillance and sanitization during regional conflicts, it is often necessary to transmit raw imagery data to a remote processing station for detection-report confirmation and more sophisticated automatic target recognition (ATR) processing. Because of the limited bandwidth available for wireless transmission, image compression is of paramount importance. Furthermore, it is equally crucial that image coding algorithms be evaluated according to some meaningful criteria. Instead of assessing the performance of image compression algorithms in terms of peak signal-to-noise ratio (PSNR) or normalized mean-squared error (NMSE), we resort to a more meaningful performance metric that reflects human and operational factors-ATR performance. We develop a novel image compression algorithm that achieves the minimal information state by a combination of subimage-specific transformation, principal component analysis, and vector quantization (VQ). We quantify the performance of image coding by extracting key parameters or features from the original and reconstructed images and by comparing the ATR performances using separate test sets that contain both mines and mine-like clutter. We achieve a compression ratio of up to 57:1 with minimal sacrifice in P/sub D/ and P/sub FA/.

Efficient quantization methods of the line spectrum pairs (LSP) which have good performances, low complexity and memory are proposed. The adaptive quantization method utilizing the ordering property of LSP parameters is used in a scalar quantizer and a vector-scalar hybrid quantizer. The maximum quantization range of each LSP parameter is varied adaptively on the quantized value of the previous order's LSP parameter. The proposed scalar quantization algorithm needs 31 bits/frame which is 3 bits less than in the conventional scalar quantization method with interframe prediction to maintain the transparent quality of speech. The improved vector-scalar quantizer achieves an average spectral distortion of 1 dB using 26 bits/frame. The performances of proposed quantization methods are evaluated in the transmission errors.

The quality of the signal is essential in digital communication and signal processing. The transmission channel is also important. Modulation is used for effectively transmission of signal. There exist several types of modulation techniques. One of such is the pulse code modulation (PCM). The performance of PCM is however affected by quantization error and noise in the transmission channel, which affects the quality of the output. Against this backdrop, this paper presents the use of differential pulse code modulation (DPCM) so as to address the limitation of pulse code modulation. The simulation environment is MATLAB 2018a. The MATLAB Simulink is used to design the PCM and DPCM systems using appropriate digital processing blocks. The DPCM system shows a significant improvement in terms of error reduction and quality of output.

This paper explains a study conducted based on wavelet packet transform techniques. In this paper the key idea underlying the construction of wavelet packet analysis (WPA) with various wavelet basis sets is elaborated. Since wavelet packet decomposition can provide more precise frequency resolution than wavelet decomposition the implementation of one dimensional wavelet packet transform and their usefulness in time signal analysis and synthesis is illustrated. A mother or basis wavelet is first chosen for five wavelet filter families such as Haar, Daubechies (Db4), Coiflet, Symlet and dmey. The signal is then decomposed to a set of scaled and translated versions of the mother wavelet also known as time and frequency parameters. Analysis and synthesis of the time signal is performed around 8 seconds to 25 seconds. This was conducted to determine the effect of the choice of mother wavelet on the time signals. Results are also prepared for the comparison of the signal at each decomposition level. The physical changes that are occurred during each decomposition level can be observed from the results. The results show that wavelet filter with WPA are useful for analysis and synthesis purpose. In terms of signal quality and the time required for the analysis and synthesis, the Haar wavelet has been seen to be the best mother wavelet. This is taken from the analysis of the signal to noise ratio (SNR) value which is around 300 dB to 315 dB for the four decomposition levels.

The wavelet transform is more and more widely used in image and video compression. One of the best known algorithms in image compression is the SPIHT algorithm which involves the wavelet transform. As today the parallelization of the wavelet transform is sufficiently investigated this work deals with the parallelization of the compression algorithm itself as a next step.

Optimization design of biorthogonal filter banks for image compression

Objective: The purpose of this work is to present the experimental results by comparing the quality of different satellite images LANDSAT 7, MODIS and ASTER after compression, using four different compression methods. Methods/Analysis: The satellite images are compressed using four different basic compression methods namely, Pulse code Modulation, Differential Pulse Code Modulation, Discrete Cosine Transform and Sub band Coding. The Compression is performed with three different types of satellite sensor images having different spectral bands, picture bit-rate and level of details using VCDemo software package. Findings: The Mean Square Error, Signal to Noise Ratio and Peak Signal to Noise Ratio values are calculated to determine the quality of the images after compression. In order to find the quality of compression methods the Mean Square Error, Signal to Noise Ratio and Peak Signal to Noise Ratio values are collected for different satellite images by compressing with different bitrates. While comparing the obtained values for each compression methods, we found that the compression methods have different impacts in each satellite image according to the bit-rate used for compression and the level of details. Conclusion: The study proves that Discrete Cosine Transform and Sub band Coding’s performance are worthy for satellite image compression. Where Sub band Coding produces a very good Signal to Noise Ratio and Peak Signal to Noise Ratio values for all bit rates. Keywords: DCT, Image Compression, MSE, PSNR, Satellite Image Compression, SBC, SNR

In machine learning problems, there are two main algorithms: supervised learning and unsupervised learning. Supervised learning algorithms can be used to classify data for tagged data; non-supervised learning algorithms can be used to cluster data for unlabeled data. This paper discusses the basic principles of clustering algorithm and selection of key parameters of clustering algorithm. The application of clustering algorithm in image compression is also analyzed. This paper also emphasizes the problems that should be paid attention to when using clustering. Finally, a practical case of image compression with K-means is given.

Many research works have been developed for stereo image compression purpose where most of them aim at encoding a reference image, a residual one and a disparity map. While the disparity field is often losslessly encoded, we are mainly interested in this paper in the bit allocation problem between the reference and residual images. Generally, the bit allocation is expressed as an optimization problem which involves the computation of the operational rate-distortion (RD) functions for all the wavelet subbands and for different quantization steps. However, this strategy is computationally intensive. To solve this problem, we consider the uniform scalar quantization of the wavelet subbands of both images modeled by a Generalized Gaussian distribution. Thanks to recent approximations of the entropy and distortion functions, we develop an optimal and fast bit allocation method. The obtained results confirm the efficiency of the proposed bit allocation method in the context of stereo image coding.

The problem of detrending the baseline noise on ECG signals particularly Normal Sinusal type is presented in this paper. A real time FPGA-based system is implemented using Wavelet filtering. For the filter design the Daubechies family is used and the implementation uses 4th order and 6 levels of decomposition. The filtered signals obtained detrending various ECG signals are compared with ideal values from simulation with satisfactory results.

Set Partitioning in Hierarchical Trees (SPIHT) is an effective image compression algorithm. SPIHT is a unique approach which transmits the values of the pixels indirectly to the coordinates. The packet coders in the images present a universal transform the framework in a constriction of the filter bank schema which is attained by the Space Frequency Quantization (SFQ) and the effective compression is achieved by the Huffman coding. The proposed scheme permits transformation in the joints and alterations in the discrete magnitude of the input image. Hence, coding performance is enhanced and the image is compressed without compromising the excellence of the image. MSPIHT is an enhanced algorithm that fits the requirement of medical image compression for storage and transmission of medical data. MSPIHT improves on the standard SPIHT in the encoding operation of wavelet coefficients to provide high quality images at low bit rates important in medical applications where accuracy is paramount. The modification is aimed at changing the set partitioning strategy by minimizing memory consumption rates, thus improving the compression ratio and speed. It also may be used at telemedicine where high image compression rate is required for the quick and efficient image transfer for diagnostics and consultations. Experimental results shows that the image compression scheme is improved with the SPIHT-SFQ based Huffman Coding scheme and it outperforms the current image compression algorithms.