Original Set Partitioning In Hierarchical Trees Research Articles

Wavelet-based image coding using saliency map

Visual information is very important in human perceiving of the surrounding world. During the observation of the considered scene, some image parts are more salient than others. This fact is conventionally addressed using the regions of interest approach. We are presenting an approach that captures the saliency information per pixel basis using one continuous saliency map for a whole image and which is directly used in the lossy image compression algorithm. Although for the encoding/decoding part of the algorithm, the notion region is not necessary anymore; the resulting method can, due to its nature, efficiently emulate large amounts of regions of interest with various significance. We provide reference implementation of this approach based on the set partitioning in hierarchical trees (SPIHT) algorithm and show that the proposed method is effective and has potential to achieve significantly better results in comparison to the original SPIHT algorithm. The approach is not limited to SPIHT algorithm and can be coupled with, e.g., JPEG 2000 as well.

Joint SPIHT compression and selective encryption

JCSE-SPIHT, an algorithm of joint compression and selective encryption based on set partitioning in hierarchical trees (SPIHT), is proposed to achieve image encryption and compression simultaneously. It can protect SPIHT compressed images by only fast scrambling a tiny portion of crucial data during the coding process while keeping all the virtues of SPIHT intact. Intensive experiments are conducted to validate and evaluate the proposed algorithm; the results show that the efficiency and the compression performance of JCSE-SPIHT are very close to original SPIHT. In security analysis, JCSE-SPIHT is proved to be immune to various attacks not only from traditional cryptanalysis, but also by utilizing sophisticated image processing techniques.

A Novel VLSI Architecture for SPHIT Encoder

In this Paper we propose a highly scalable image compression scheme based on the set partitioning in hierarchical trees (SPIHT) algorithm. Our algorithm called highly scalable SPIHT (HS-SPIHT), supports spatial and SNR scalability and provides a bit stream that can be easily adapted (reordered) to given bandwidth and resolution requirements by a simple transcoder (parser). The HS-SPIHT algorithm adds the spatial scalability feature without sacrificing the SNR embeddedness property as found in the original SPIHT bit stream. HS-SPIHT finds applications in progressive Web browsing, flexible image storage and retrieval, and image transmission over heterogeneous networks. Here we have written the core processor Microblaze is designed in VHDL (VHSIC hardware description language), implemented using XILINX ISE 8.1 Design suite the algorithm is written in system C Language and tested in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC using the RS232 cable. The test results are seen to be satisfactory. The area taken and the speed of the algorithm are also evaluated.

A Block-Based Pass-Parallel SPIHT Algorithm

Set-partitioning in hierarchical trees (SPIHT) is a widely used compression algorithm for wavelet-transformed images. One of its main drawbacks is a slow processing speed due to its dynamic processing order that depends on the image contents. To overcome this drawback, this paper presents a modified SPIHT algorithm called block-based pass-parallel SPIHT (BPS). BPS decomposes a wavelet-transformed image into 4 × 4 blocks and simultaneously encodes all the bits in a bit-plane of a 4 × 4 block. To exploit parallelism, BPS reorganizes the three passes of the original SPIHT algorithm and then BPS encodes/decodes the reorganized three passes in a parallel and pipelined manner. The precalculation of the stream length of each pass enables the parallel and pipelined execution of these three passes by not only an encoder but also a decoder. The modification of the processing order slightly degrades the compression efficiency. Experimental results show that the peak signal-to-noise ratio loss by BPS is between approximately 0.23 and 0.59 dB when compared to the original SPIHT algorithm. Both an encoder and a decoder are implemented in the hardware that can process 120 million samples per second at an operating clock frequency of 100 MHz. This processing speed allows a video of size of 1920 × 1080 in the 4:2:2 format to be processed at the rate of 30 frames/s. The gate count of the hardware is about 43.9 K.

A Hybrid Scheme Using Improved SPIHT and Huffman Coding for Lossless Image Compression

In order to improve the coding performance of image lossless compression, a hybrid lossless compression scheme based on modified Set Partitioning in Hierarchical Trees (SPIHT) and Huffman coding is proposed. The lower magnitude bit-planes, whose corresponding magnitude thresholds are less than 8, and the sign bit-plane are scanned node by node in the fixed order, and the bit sequence obtained by scanning the bit-planes is further processed by Huffman coder to remove the statistical redundancy. The other bit-planes, namely the higher magnitude bit-planes, are sorted by the modified SPIHT partitioning structure, which could improves the coding efficiency of sorting pass by means of grouping more nodes together. Experimental results show the proposed algorithm has reduced its bit-rate by 0.13bit/pixel-0.35bit/pixel for gray images and by 0.72bit/pixel-1.25bit/pixel for color images as compared with those of the original SPIHT algorithm. Furthermore, the modified scheme retains the progressive transmission property of SPIHT coder. It is concluded that the proposed method efficiently reduces the redundancy existing in the original SPIHT coder and is fit for lossless image compression.

Combined significance map coding for still image compression

Set partitioning in hierarchical trees (SPIHT) was known for its relatively simple implementation and flexible scalability when it is combined with discrete wavelet transform (DWT). The authors propose a method called combined significance map coding (CSMC) to improve the coding efficiency of SPIHT when used with block-based discrete cosine transform (DCT). CSMC groups some blocks and encodes the combined significance map of one to several blocks together. Lots of bits spent in significance map coding can be saved when the trees constructed with block DCT coefficients have similar locality. From our simulation results, CSMC improves significantly when in comparison with the original SPIHT coder using DWT and DCT. It also yields better performance than JPEG2000, and even outperforms the non-scalable H.264 intra-mode coder for some test images. No coding table is required, and fine rate/quality scalability property of SPIHT is still preserved.

A Fast and Memory Efficient SPIHT Image Encoder

Set-partitioning in hierarchical trees (SPIHT) is one of the well-known image compression schemes. SPIHT offers an agreeable compression ratio and produces an embedded bit-stream for progressive transmission. However, the major disadvantage of SPIHT is its large memory requirement. In this paper, we propose a memory efficient SPIHT image coder and its parallel implantation. The memory requirement is reduced without sacrificing image quality. All bit-planes are concurrently encoded in order to speed up the entire coding flow. The result shows that the proposed algorithm is roughly 6 times faster than the original SPIHT. For a 512 × 512 image, the memory requirement is reduced from 5.83Mb to 491Kb. The proposed algorithm is also realized on FPGA. With pipeline design, the circuit can run at 110MHz, which can encode a 512 × 512 image in 1.438ms. Thus, the circuit achieves very high throughput, 182MPixels/sec, and can be applied to high performance image compression applications.

Efficient FPGA implementation of DWT and modified SPIHT for lossless image compression

In this paper, we present an implementation of the image compression technique set partitioning in hierarchical trees (SPIHT) in programmable hardware. The lifting based Discrete Wavelet Transform (DWT) architecture has been selected for exploiting the correlation among the image pixels. In addition, we provide a study on what storage elements are required for the wavelet coefficients. A modified SPIHT (Set Partitioning in Hierarchical Trees) algorithm is presented for encoding the wavelet coefficients. The modifications include a simplification of coefficient scanning process, use of a 1-D addressing method instead of the original 2-D arrangement for wavelet coefficients and a fixed memory allocation for the data lists instead of the dynamic allocation required in the original SPIHT. The proposed algorithm has been illustrated on both the 2-D Lena image and a 3-D MRI data set and is found to achieve appreciable compression with a high peak-signal-to-noise ratio (PSNR).

Medical images compression for remote diagnosis using modified SPIHT data organization and fidelity enhancement filter

AbstractIn this work, an 8 × 8 Discrete Cosine Transform (DCT) approach is adopted to perform DCT shrinkage, followed by modified set partitioning in hierarchical trees (SPIHT) data organization and fidelity enhancement filter for reducing the memory required to store a remote diagnosis and rapidly transmit it. The DCT shrinkage has the ability to retain the detailed characteristics of an image. By means of a simple transformation to gather the DCT spectrum data with the same frequency domain, the DCT shrinkage exploits all the characteristics of individual blocks to a global framework. In this scheme, insignificant DCT coefficients that correspond to the same spatial location in the high‐frequency sub‐bands can be used to reduce the redundancy by a combined function proposed in association with the modified SPIHT. Meanwhile, quad‐tree decomposition and a set of morphological filters for reducing the artifacts are presented. This set of filters employs 8 predefined morphological operations, namely 4 structuring elements (SE), each of which includes both dilation and erosion operations. The voting strategy is used to select the most suitable morphological filter for each block. Simulation results show that the image compression reduced the computational complexity to only a half of the wavelet based sub‐band decomposition and improved the quality of the reconstructed medical image in terms of both the peak signal‐to‐noise ratio (PSNR) and the perceptual results close to JPEG2000 and the original SPIHT at the same bit rate. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 49–61, 2007

VLSI Implementation of a Modified Efficient SPIHT Encoder

Set Partitioning in Hierarchical Trees (SPIHT) is a highly efficient technique for compressing Discrete Wavelet Transform (DWT) decomposed images. Though its compression efficiency is a little less famous than Embedded Block Coding with Optimized Truncation (EBCOT) adopted by JPEG2000, SPIHT has a straight forward coding procedure and requires no tables. These make SPIHT a more appropriate algorithm for lower cost hardware implementation. In this paper, a modified SPIHT algorithm is presented. The modifications include a simplification of coefficient scanning process, a 1-D addressing method instead of the original 2-D arrangement of wavelet coefficients, and a fixed memory allocation for the data lists instead of a dynamic allocation approach required in the original SPIHT. Although the distortion is slightly increased, it facilitates an extremely fast throughput and easier hardware implementation. The VLSI implementation demonstrates that the proposed design can encode a CIF (352 × 288) 4:2:0 image sequence with at least 30 frames per second at 100-MHz working frequency.

Medical image compression using DCT-based subband decomposition and modified SPIHT data organization

Objective The work proposed a novel bit-rate-reduced approach for reducing the memory required to store a remote diagnosis and rapidly transmission it. Method In the work, an 8 × 8 Discrete Cosine Transform (DCT) approach is adopted to perform subband decomposition. Modified set partitioning in hierarchical trees (SPIHT) is then employed to organize data and entropy coding. The translation function can store the detailed characteristics of an image. A simple transformation to obtain DCT spectrum data in a single frequency domain decomposes the original signal into various frequency domains that can further compressed by wavelet-based algorithm. In this scheme, insignificant DCT coefficients that correspond to a particular spatial location in the high-frequency subbands can be employed to reduce redundancy by applying a proposed combined function in association with the modified SPIHT. Results and conclusions Simulation results showed that the embedded DCT-CSPIHT image compression reduced the computational complexity to only a quarter of the wavelet-based subband decomposition, and improved the quality of the reconstructed medical image as given by both the peak signal-to-noise ratio (PSNR) and the perceptual results over JPEG2000 and the original SPIHT at the same bit rate. Additionally, since 8 × 8 fast DCT hardware implementation being commercially available, the proposed DCT-CSPIHT can perform well in high speed image coding and transmission.

A modified SPIHT algorithm for image coding with a joint MSE and classification distortion measure

The set partitioning in hierarchical trees (SPIHT) algorithm is an efficient wavelet-based progressive image-compression technique, designed to minimize the mean-squared error (MSE) between the original and decoded imagery. However, the MSE-based distortion measure is not in general well correlated with image-recognition quality, especially at low bit rates. Specifically, low-amplitude wavelet coefficients that may be important for classification are given low priority by conventional SPIHT. In this paper, we use the kernel matching pursuits (KMP) method to autonomously estimate the importance of each wavelet subband for distinguishing between different textures, with textural segmentation first performed via a hidden Markov tree. Based on subband importance determined via KMP, we scale the wavelet coefficients prior to SPIHT coding, with the goal of minimizing a Lagrangian distortion based jointly on the MSE and classification error. For comparison we consider Bayes tree-structured vector quantization (B-TSVQ), also designed to obtain a tradeoff between MSE and classification error. The performances of the original SPIHT, the modified SPIHT, and B-TSVQ are compared.

SPIHT image compression on FPGAs

In this paper, we present an implementation of the image compression routine set partitioning in hierarchical trees (SPIHT) in reconfigurable logic. A discussion on why adaptive logic is required, as opposed to an application specific integrated circuit (ASIC), is provided along with background material on the image compression algorithm. We analyzed several discrete wavelet transform (DWT) architectures and selected the folded DWT design. In addition we provide a study on what storage elements are required for each wavelet coefficient. A modification to the original SPIHT algorithm is implemented to parallelize the computation. The architecture of our SPIHT engine is based upon fixed-order SPIHT, developed specifically for use within adaptive hardware. For an N/spl times/N image fixed-order SPIHT may be calculated in N/sup 2//4 cycles. Square images which are powers of 2 up to 1024/spl times/1024 are supported by the architecture. Our system was developed on an Annapolis Microsystems WildStar board populated with Xilinx Virtex-E parts. The system achieves a 450/spl times/ speedup versus a microprocessor solution, with less than a 0.2-db loss in peak signal-to-noise ratio over traditional SPIHT.

Flexible, highly scalable, object-based wavelet image compression algorithm for network applications

The authors propose a highly scalable image compression scheme based on the set partitioning in hierarchical trees (SPIHT) algorithm. The proposed algorithm, called highly scalable SPIHT (HS-SPIHT), adds the spatial scalability feature to the SPIHT algorithm through the introduction of multiple resolution-dependent lists and a resolution-dependent sorting pass. It keeps the important features of the original SPIHT algorithm such as compression efficiency, full SNR scalability and low complexity. The flexible bitstream of the HS-SPIHT encoder can easily be adapted to various resolution requirements at any bit rate. The parsing process can be carried out on-the-fly without decoding the bitstream by a simple parser (transcoder) that forms a part of a smart network. The HS-SPIHT algorithm is further developed for fully scalable coding of arbitrarily shaped visual objects. The proposed highly scalable algorithm finds applications in progressive web browsing, visual databases and especially in image transmission over heterogeneous networks.

Performance improvement of the SPIHT coder

The set partitioning in hierarchical trees (SPIHT) coder is one of the state-of-the-art coders among the wavelet-based image compression coders. For improving the performance of the SPIHT coder, in this paper, we propose a pre-processing method that applies the discrete sine transform or the discrete cosine transform to the wavelet coefficients in the highest frequency subbands and in the next highest frequency subbands before the SPIHT encoding. Experimental results show that the proposed method increases the peak signal to noise ratio by up to 0.4 (dB) in textured images over the original SPIHT coder.

New high-fidelity medical image compression based on modified set partitioning in hierarchical trees

Medical images must be compressed before transmission due to bandwidth and storage limitations. The set partitioning in hierar- chical trees (SPIHT) algorithm is an efficient method for lossy and loss- less coding of medical images. We propose some modifications to the SPIHT algorithm. It is based on the idea of the insignificant correlation of wavelet coefficients among medium- and high-frequency subbands. In this scheme, insignificant wavelet coefficients that correspond to the same spatial location in the medium subbands can be used to reduce the redundancy by a combined function proposed in associated with the modified SPIHT. In high-frequency subbands, the modified SPIHT pro- poses a dictator to reduce the interband redundancy. Experimental re- sults indicate that the proposed technique improves the quality of the reconstructed medical image in terms of both the peak signal-to-noise ratio (PSNR) and the perceptual results over JPEG2000 and the original SPIHT at the same bit rate. © 2003 Society of Photo-Optical Instrumentation Engi- neers. (DOI: 10.1117/1.1578645)

Significance map pruning and other enhancements to SPIHT image coding algorithm

This paper proposes several enhancements to the Set Partitioning in Hierarchical Trees (SPIHT) image coding algorithm without changing the original algorithm's general skeleton. First and foremost, a method for significance map pruning based on a rate-distortion criterion is introduced. Specifically, the (Type A) sets of wavelet coefficients with small ratios of estimated distortion reduction to estimated rate contribution are deemed insignificant and effectively pruned. Even though determining such sets requires the computational complexity of the encoder to increase considerably with respect to the original SPIHT encoder, the original SPIHT decoder may still be used to decode the generated bitstream with a low computational complexity. The paper also proposes three low complexity enhancements by more sophisticated use of the adaptive arithmetic coder. Simulation results demonstrate that all these enhancements yield modest compression gains at moderate to high rates.

Real-time implementation of a new low-memory SPIHT image coding algorithm using DSP chip

Among all algorithms based on wavelet transform and zerotree quantization, Said and Pearlman's (1996) set partitioning in hierarchical trees (SPIHT) algorithm is well-known for its simplicity and efficiency. This paper deals with the real-time implementation of SPIHT algorithm using DSP chip. In order to facilitate the implementation and improve the codec's performance, some relative issues are thoroughly discussed, such as the optimization of program structure to speed up the wavelet decomposition. SPIHT's high memory requirement is a major drawback for hardware implementation. In this paper, we modify the original SPIHT algorithm by presenting two new concepts-number of error bits and absolute zerotree. Consequently, the memory cost is significantly reduced. We also introduce a new method to control the coding process by number of error bits. Our experimental results show that the implementation meets common requirement of real-time video coding and is proven to be a practical and efficient DSP solution.

A multiresolutional coding method based on SPIHT

We propose a new method to code still images with multiresolutional coding functionality. This algorithm is based on set partitioning in hierarchical trees (SPIHT) (IEEE Trans. Circuits Systems Video Technol. 6 (3) (1996) 243). We consider each subband as a region of interest and encode it using a region of interest (ROI) coding scheme proposed by the authors. The ROI coding scheme introduced a new parent ROI mask and multiple lists of insignificant pixels and multiple lists of insignificant sets. The proposed multiresolutional coding handles multiple ROIs, each of which corresponds to each subband of the wavelet-transformed image. Although the PSNR performance of the proposed multiresolutional coding at low rates is inferior to the original SPIHT algorithm, it provides multiresolutional coding functionality. We analyzed the rate-distortion performance and the computation time of the multiresolutional coding with respect to various bit-rates.

Hybrid image compression model based on subband coding and edge-preserving regularisation

An edge-preserving image compression model is presented, based on subband coding and iterative constrained least square regularisation. The idea is to incorporate the technique of image restoration into the current lossy image compression schemes. The model utilises the edge information extracted from the source image as a priori knowledge for the subsequent reconstruction. Generally, the extracted edge information has a limited range of magnitudes and it can be lossily conveyed. Subband coding, one of the outstanding lossy image compression schemes, is incorporated to compress the source image. Vector quantisation, a block-based lossy compression technique, is employed to compromise the bit rate incurred by the additional edge information and the target bit rate. Experiments show that the approach could significantly improve both the objective and subjective quality of the reconstructed image by preserving more edge details. Specifically, the model incorporated with SPIHT (set partitioning in hierarchical trees) outperformed the original SPIHT with the Baboon continuous-tone test image. In general, the model may be applied to any lossy image compression systems.