A Hybrid Image Compression Algorithm based on Human visual system

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.

According to the designed characteristics and demands of aeronautic image compression system, lifting scheme wavelet and SPIHT algorithm was selected as the key part of software implementation, which was introduced with details. In order to improve execution efficiency, border processing was simplified reasonably and SPIHT (Set Partitioning in Hierarchical Trees) algorithm was also modified partly. The results showed that the selected scheme has a 0.4dB improvement in PSNR(peak-peak-ratio) compared with classical Shaprio's scheme. To improve the operating speed, the hardware system was then designed based on DSP and many optimization measures were then applied successfully. Practical test showed that the system can meet the real-time demand with good quality of reconstruct image, which has been used in an aeronautic image compression system practically.

Recently many new algorithms for image compression based on wavelets have been developed.This paper gives a detailed explanation of SPIHT algorithm with the combination of Lempel Ziv Welch compression technique for image compression by MATLAB implementation. Set partitioning in Hierarchical trees (SPIHT) is one of the most efficient algorithm known today. Pyramid structures have been created by the SPIHT algorithm based on a wavelet decomposition of an image. Lempel Ziv Welch is a universal lossless data compression algorithm guarantees that the original information can be exactly reproduced from the compressed data.The proposed methods have better compression ratio, computational speed and good reconstruction quality of the image. To analysis the proposed lossless methods here, calculate the performance metrics as Compression ratio, Mean square error, Peak signal to Noise ratio. Key Words-LempelZivWelch (LZW),SPIHT,Wavelet

This chapter is devoted to the exposition of a complete video coding system, which is based on coding of three dimensional (wavelet) subbands with the SPIHT (set partitioning in hierarchical trees) coding algorithm. The SPIHT algorithm, which has proved so successful in still image coding, is also shown to be quite effective in video coding, while retaining its attributes of complete embeddedness and scalability by fidelity and resolution. Three-dimensional spatio-temporal orientation trees coupled with powerful SPIHT sorting and refinement renders 3D SPIHT video coder so efficient that it provides performance superior to that of MPEG-2 and comparable to that of H.263 with minimal system complexity. Extension to color-embedded video coding is accomplished without explicit bit-allocation, and can be used for any color plane representation. In addition to being rate scalable, the proposed video coder allows multiresolution scalability in encoding and decoding in both time and space from one bit-stream. These attributes of scalability, lacking in MPEG-2 and H.263, along with many desirable features, such as full embedded-ness for progressive transmission, precise rate control for constant bit-rate (CBR) traffic, and low-complexity for possible software-only video applications, makes the proposed video coder an attractive candidate for for multi-media applications. Moreover, the codec is fast and efficient from low to high rates, obviating the need for a different standard for each rate range.

We investigate the issue of efficient data organization and representation of the curved wavelet coefficients [curved wavelet transform (WT)]. We present an adaptive zero-tree structure that exploits the cross-subband similarity of the curved wavelet transform. In the embedded zero-tree wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT), the parent-child relationship is defined in such a way that a parent has four children, restricted to a square of 2×2 pixels, the parent-child relationship in the adaptive zero-tree structure varies according to the curves along which the curved WT is performed. Five child patterns were determined based on different combinations of curve orientation. A new image coder was then developed based on this adaptive zero-tree structure and the set-partitioning technique. Experimental results using synthetic and natural images showed the effectiveness of the proposed adaptive zero-tree structure for encoding of the curved wavelet coefficients. The coding gain of the proposed coder can be up to 1.2 dB in terms of peak SNR (PSNR) compared to the SPIHT coder. Subjective evaluation shows that the proposed coder preserves lines and edges better than the SPIHT coder.

Embedded zero-tree image coding in wavelet domain has drawn a lot of attention. Among noteworthy algorithms is the set partitioning in hierarchical trees (SPIHT). Typically, most of images' energy is concentrated in low frequency subbands. For an image with textures, however many middle-high frequency wavelet coefficients are likely to become significant in the early passes of SPIHT; thus the coding results are often insufficient. Middle and high frequency subbands of images may demand further decompositions using adaptive basis functions. As wavelet packet transform offers a great diversity of basis functions, we propose a quad-tree based adaptive wavelet packet transform to construct adaptive wavelet packet trees for zero-tree image coding. Experimental results show that coding performances can be significantly improved especially for fingerprints images.

Set partitioning in hierarchical trees (SPIHT) image compression algorithm based on wavelet transform can't effectively express the image of the texture and contour. Contourlet transform can retain image details better because it avoids down sampling to high-frequency subband, and the coefficient is also sparser than it of wavelet transform. In this paper, a suitable SPIHT algorithm for contourlet transform was proposed for its coefficient matrix characteristics and distribution. The new algorithm not only overcome the disadvantage of not protecting details and texture effectively when using wavelet transform, but also saves coding time, at the same time enhance the image of the peak signal to noise ratio (PSNR). Simulation results show that the method is an efficient image compression algorithm. Its compression effect is obviously better than traditional SPIHT algorithms based on wavelet transform and WBCT (especially at low bit rates). It has generality.

This study presents a multiple description coding (MDC) with WiMAX modulations for color images. The proposed approach uses the set partitioning in hierarchical trees (SPIHT) algorithm to compress images, employs a poly-phase sampling technique to generate multiple descriptions and develops a transmitting principle for WiMAX to adapt to varying wireless network environments. As a result, our proposed method achieves high performance of image compression, obtains superior capabilities of conquering packet losses and has excellent quality of reconstructed images. Besides, by encoding the three components (Red, Green, and Blue) separately and interleaving the produced encoding results for transmission it extends progressive reconstruction for a color image. Furthermore, the proposed method is highly flexible for practical applications. Extensive experiments are performed and their results demonstrate promising characteristics and performances of the proposed method.

In this paper, an improved method for electrocardiogram (ECG) signal compression using Set Partitioning in Hierarchical Trees (SPIHT) algorithm is proposed. ECG signals are compressed based on different transform such as discrete cosine transform and discrete wavelet transform with modified SPIHT. The modified SPIHT algorithm yields good compression with controlled quantity of signal degradation and requires computational time as compared to earlier published SPIHT algorithms. The proposed algorithm is suitable for the ECG signal compression for telemedicine or e-health system due to minimum computational time.

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.

For purpose of improving the quality of multi-source fusion images, this paper introduces a fusion method of infrared and visible images based on the combination of the non-subsampled contourlet transform (NSCT) and the improved pulse coupled neural network model (PCNN). To begin with, registered images are decomposed by NSCT, the low frequency and high frequency sub-bands are also obtained. Then, the energy of Laplacian (SML) is used for the link intensity of PCNN, computing spatial frequency (SF) with a low frequency sub-band, and the gradient energy (EOG) with a high frequency sub-band. By means of treating them as external input of PCNN. Then fused coefficients are selected by sum of ignition output amplitude maximum, a fused image is finally obtained by inverse NSCT. Experimental results show that the fusion image has a high definition and effectively preserves information of the source image.

An efficient compression strategy is indispensable for 3-D digital echocardiography, which can provide more accurate di- agnostic information than 2-D echocardiography without geometric assumption, but unfortunately requires a huge storage space. We describe a new set partitioning in hierarchical trees (SPIHT) algo- rithm that is based on the wavelet packet transform and use it to compress 3-D echocardiographic images. The new algorithm origi- nates from the well-known SPIHT algorithm that is based on a wavelet transform but outperforms it for the compression of 3-D echocardiographic images. Experimental results are presented to verify the effectiveness of the new SPIHT algorithm. Results show that the new algorithm achieves a very high compression ratio while retaining good quality 3-D echocardiographic images. © 2006 SPIE

In the domains that deal with a large scale image applications force the use of image compression in order to reduce the required storage. Due to the constrained bandwidth and storage capacity in the field of marine a new compression technique for the compression of the satellite images is proposed here. In our system, we are separating the region of Region of Interest (ROI) part from the original image. And this ROI images can be used for compression scheme by an algorithm known as the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The SPIHT algorithm is a part of the Lossless compression algorithm. The performance of our method can be done by calculating the PSNR values of the output images.

This paper proposes a pan-sharpening method based on multi-direction subbands and deep neural networks. First, by utilizing the multi-scale and multi-direction properties of the nonsubsampled contourlet transform (NSCT), panchromatic (PAN) image is decomposed into the low frequency subbands in different resolutions and the high frequency subbands in different directions. Pan-sharpening method aims to fuse the high frequency subband coefficients of PAN image and the low frequency subband coefficients of multispectral (MS) image. Second, in order to better extract the feature of the high frequency subbands in different directions of PAN image, the deep neural network (DNN) is trained using the image patches of high frequency subbands of PAN image. Third, in the fusion stage, we exploit NSCT on the principal component of resampled low resolution (LR) MS image. The high frequency subbands of output high resolution (HR) MS image is obtained by forward propagation of the trained DNN, which input is the high frequency subbands of LR MS image. Finally, a new subband set is obtained by fusing the reconstructed high frequency subband and the original low frequency subband of LR MS image. The HR MS image is produced by executing the inverse transform of NSCT and adaptive PCA (A-PCA) on the new subband set. The experimental results show the proposed method outperforms other well-known methods in terms of both objective measurements and visual evaluation.

Two compression frameworks that are based on a set partitioning in hierarchical trees (SPIHT) and JPEG2000 methods are proposed. The 3D mesh is first transformed to 2D images on a regular grid structure. Then, this image-like representation is wavelet transformed employing an adaptive predictor that takes advantage of the connectivity information of mesh vertices. Then SPIHT or JPEG2000 is applied on the wavelet domain data. The SPIHT based method is progressive because the resolution of the reconstructed mesh can be changed by varying the length of the one-dimensional data stream created by SPIHT algorithm. The results of the SPIHT based algorithm is observed to be superior to JPEG2000 based mesh coder and MPEG-3DGC in rate-distortion.