Wavelet Image Coding Research Articles

MULTI-LEVEL WAVELET BASED IMAGE CODING OVER LIFTING SCHEME FOR EMBEDDED WIRELESS DEVICES

This paper provides a concise synopsis in the application of Lifting Scheme (LS) in wavelet image coding. Lifting theorem was applied via LS 5/3 wavelet transform to develop a design wherein multipliers are replaced with shifters, thus lowering the volume of operations entailed in the process of computing a DWT to approximately one-half of the requisite volumes of a convolution approach. Consequently, the computations required for image coding are reduced and less intricate. In addition, the lifting scheme can be modified to meet the demands of in-place computation, for DWT to be implemented in low memory systems, which presents a unique solution to the issues associated with existing time consuming software algorithms. This LS filter consists of integer adder units and binary shifter instead of multiplier and divider units found in convolution based filters; thus it has been modified to provide energy efficient hardware performance. The low power 5/3 LS based wavelet transform involves solutions developed to resolve the energy and bandwidth communication problems related to image data transmission. These solutions entail constructing 2D-DWT image codec architecture to save the computational and communication energy erstwhile dissipated in existing architectures.

Scalable line-based wavelet image coding in wireless sensor networks

Existing scalable wavelet image coding approaches, such as set partitioning in hierarchical trees and its derivatives, employ a memory-intensive tree-based coding structure. Existing tree-based wavelet coding approaches are therefore not suitable for memory-constrained sensor nodes. In this paper, we introduce a scalable wavelet image coding approach based on a line structure that requires very little memory. The proposed line-based approach is suitable for scalable wavelet image coding in memory-constrained sensor nodes, requiring only a few kilobytes of memory for a 256×256 pixel image. The presented line-based wavelet coding algorithm accesses the image data line by line and thus conforms with the data access patterns in current flash memory technology. Our performance evaluations demonstrate that the proposed scalable line-based image wavelet coding approach has no overhead compared to one-run (non-scalable) wavelet image coding and has competitive compression performance compared to JPEG 2000 and the recent Google WebP image format.

ZM-SPECK: A Fast and Memoryless Image Coder for Multimedia Sensor Networks

The set partitioned embedded block (SPECK) algorithm is an efficient block-based image coder to encode wavelet transformed images. SPECK uses linked lists to track significant/insignificant coefficients and block sets, thereby having a large memory requirement that increases with the encoding rate. Furthermore, multiple memory read/write operations and list management slow down the algorithm. In addition, the implementation of the traditional discrete wavelet transform (DWT) is memory intensive and time-consuming. Therefore, it is difficult to implement image coding using the traditional DWT and SPECK algorithm on low-cost visual sensor nodes. Most of the existing studies on low-memory implementations of the SPECK algorithm attempt to replace the dynamic memory of linked lists by a static memory in the form of fixed-length state tables/markers. In this paper, a fast and memoryless image coder is proposed, which uses the fractional wavelet filter to calculate the DWT coefficients of the image and a zero-memory listless SPECK algorithm for quantization and coding of the DWT coefficients. The proposed algorithm, referred as zero-memory SPECK (ZM-SPECK), completely eliminates the linked lists and only uses a few registers to perform some low-level arithmetic/logical operations. The elimination of linked lists also reduces the memory access time, thereby making ZM-SPECK faster than the original SPECK algorithm. Simulation results show that the proposed ZM-SPECK coder outperforms the contemporary state-of-the-art wavelet image coders in terms of memory requirement and computational complexity, while retaining their coding efficiency. The proposed ZM-SPECK image coder is thus very well suited for image communication in visual sensor networks.

An Effective Image Coding Method using Lattice Vector Quantization in Wavelet Domain

As the digital information must be stored and recovered in an effective ways, it is necessary to compress data in image coding, wavelet transform and lattice vector quantization are adopted in this paper to improve the compression effective of image coding. We first propose the two dimensional spatial wavelet decomposition of image, and then we present lattice Vector Quantization coding scheme for each image subband, lattice vector quantization can make full use of the correlation between the image wavelet coefficients to remove the information redundancy, and the coding method of lattice quantization can be effective because of applying the symmetries of the lattice. The establishment of rate distortion (RD) model suitable for lattice vector quantization of wavelet image coder is also important for image compression, study shows that the RD performance for the spatial subbands are fitted by an exponential form theoretical model, this yields an analytical solution to the bit rate distribution issue, we explore an effective rate control scheme by using the lagrangian optimization method to distribute the bit rate for the spatial subbands. The experimental results show that the proposed algorithm can achieve better compressing effect with minimum loss.

Application of Embedded Wavelet Image Coding Algorithm in Track and Field Route Optimization

Embedded wavelet image coding algorithm has excellent localization property in time domain and frequency domain. To some extent, it can fix position for graphic information which has different direction features to any precision level. At the same time, with the disappear of blocking effect and noise, it is able to perfectly match with the visual features of human beings and it has quickly become one of the hot research direction in the field of image processing. This paper firstly defines the wavelet transform and elaborates the principle and connotation of embedded wavelet algorithm. And then, this paper reconstructs the image wavelet. On the basis of this, wavelet algorithm is transformed in frequency domain. At the same time, this paper constructs the image fusion model which is based on embedded wavelet image coding algorithm and further applies the edge detection and image fusion of the model to the track route. Analog simulation is also made in the application of the algorithm and the effect of real virtual composite is obvious. To some extent, it provides new exploration ideas and practice path for the research in this field.

Enhancing LTW image encoder with perceptual coding and GPU-optimized 2D-DWT transform

When optimizing a wavelet image coder, the two main targets are to (1) improve its rate-distortion (R/D) performance and (2) reduce the coding times. In general, the encoding engine is mainly responsible for achieving R/D performance. It is usually more complex than the decoding part. A large number of works about R/D or complexity optimizations can be found, but only a few tackle the problem of increasing R/D performance while reducing the computational cost at the same time, like Kakadu, an optimized version of JPEG2000. In this work we propose an optimization of the E_LTW encoder with the aim to increase its R/D performance through perceptual encoding techniques and reduce the encoding time by means of a graphics processing unit-optimized version of the two-dimensional discrete wavelet transform. The results show that in both performance dimensions, our enhanced encoder achieves good results compared with Kakadu and SPIHT encoders, achieving speedups of 6 times with respect to the original E_LTW encoder.

Wavelet based Image Coding Schemes: A Recent Survey

A variety of new and powerful algorithms have been developed for image compression over the years. Among them the wavelet-based image compression schemes have gained much popularity due to their overlapping nature which reduces the blocking artifacts that are common phenomena in JPEG compression and multiresolution character which leads to superior energy compaction with high quality reconstructed images. This paper provides a detailed survey on some of the popular wavelet coding techniques such as the Embedded Zerotree Wavelet (EZW) coding, Set Partitioning in Hierarchical Tree (SPIHT) coding, the Set Partitioned Embedded Block (SPECK) Coder, and the Embedded Block Coding with Optimized Truncation (EBCOT) algorithm. Other wavelet-based coding techniques like the Wavelet Difference Reduction (WDR) and the Adaptive Scanned Wavelet Difference Reduction (ASWDR) algorithms, the Space Frequency Quantization (SFQ) algorithm, the Embedded Predictive Wavelet Image Coder (EPWIC), Compression with Reversible Embedded Wavelet (CREW), the Stack-Run (SR) coding and the recent Geometric Wavelet (GW) coding are also discussed. Based on the review, recommendations and discussions are presented for algorithm development and implementation.

웨이블릿 계수의 비트 레벨 정보를 사용한 영상 부호화 기법

본 논문에서는 웨이블릿 계수의 비트 레벨 정보를 사용한 영상 부호화 기법을 제안한다. 제안한 방법은 수정된 EZW 알고리즘과 부호화 과정 시 요구되는 메모리 사용량을 줄이기 위해 웨이블릿 계수들의 비트 레벨 정보를 가지는 중요 계수 배열을 사용한다. 중요 계수 배열은 웨이블릿 계수의 비트 레벨 정보를 저장하는 이차원 자료 구조로서, 제안된 알고리즘은 이것을 이용하여 중요한 계수에 대한 부호화 과정과 계수들의 비트 레벨 정보의 부호화 과정을 한 번에 수행할 수 있다. 실험 결과 기존의 부호화 방법보다 화질 면에서 비슷하거나 우수한 성능을 보인다. 특히 중요 계수 배열은 이용한 최소의 메모리 사용으로 다양한 비트율에서 영상의 일그러짐 없이 안정적으로 동작함을 확인하였다. In this paper, the wavelet image coder, that can encode the bit-level information of wavelet coefficients, is proposed. The proposed coder is used the modified EZW algorithm and significant coefficient array that has bit level information of the wavelet coefficients to reduce the memory requirement in coding process. The significant coefficient array is two dimensional data structure that has bit level information of the wavelet coefficients. The proposed algorithm performs the coding of the significance coefficients and coding of bit level information of wavelet coefficients at a time by using the significant coefficient array. Experimental results show a better or similar performance of the proposed method when compared with conventional embedded wavelet coding algorithm. Especially, the proposed algorithm performs stably without image distortion at various bit rates with minimum memory usage by using the significant coefficient array.

Image‐Based Surface Compression

Abstract We present a generic framework for compression of densely sampled three‐dimensional (3D) surfaces in order to satisfy the increasing demand for storing large amounts of 3D content. We decompose a given surface into patches that are parameterized as elevation maps over planar domains and resampled on regular grids. The resulting shaped images are encoded using a state‐of‐the‐art wavelet image coder. We show that our method is not only applicable to mesh‐ and point‐based geometry, but also outperforms current surface encoders for both primitives.

A wavelet packet based block-partitioning image coding algorithm with rate-distortion optimization

As an elegant generalization of wavelet transform, wavelet packet (WP) provides an effective representation tool for adaptive waveform analysis. Recent work shows that image-coding methods based on WP decomposition can achieve significant gain over those based on a usual wavelet transform. However, most of the work adopts a tree-structured quantization scheme, which is a successful technique for wavelet image coding, but not appropriate for WP subbands. This paper presents an image-coding algorithm based on a rate-distortion optimized wavelet packet decomposition and on an intraband block-partitioning scheme. By encoding each WP subband separately with the block-partitioning algorithm and the JPEG2000 context modeling, the proposed algorithm naturally avoids the difficulty in defining parent-offspring relationships for the WP coefficients, which has to be faced when adopting the tree-structured quantization scheme. The experimental results show that the proposed algorithm significantly outperforms SPIHT and JPEG2000 schemes and also surpasses state-of-the-art WP image coding algorithms, in terms of both PSNR and visual quality.

Dilation-run wavelet image coding

The run-length coding and the morphological representation are two classical schemes for wavelet image coding. The run-length coders have the advantage of simplicity by recording the lengths of zero-runs between significant wavelet coefficients but at the expense of yielding an inferior rate-distortion performance. The morphology-based coders, on the other hand, utilize the morphological dilation operation to delineate the clusters of significant coefficients for improving coding performance. In this paper, a novel dilation-run image coding algorithm is developed by taking the advantages of both schemes, in which the clustered significant coefficients are extracted by using the morphological dilation operation and the insignificant coefficients between the extracted clusters are coded by using the run-length coding method. The proposed dilation-run image coder is implemented in the framework of bitplane coding for producing embedded bitstreams. Compared with several state-of-the-art wavelet image coding methods, the proposed dilation-run image coding method achieves comparable rate-distortion coding performance, especially more attractive for fingerprint type of imageries.

REGULARITY SCALABLE IMAGE CODING BASED ON WAVELET SINGULARITY DETECTION

In this paper, we propose an adaptive algorithm for scalable wavelet image coding, which is based on the general feature, the regularity, of images. In pattern recognition or computer vision, regularity of images is estimated from the oriented wavelet coefficients and quantified by the Lipschitz exponents. To estimate the Lipschitz exponents, evaluating the interscale evolution of the wavelet transform modulus sum (WTMS) over the directional cone of influence was proven to be a better approach than tracing the wavelet transform modulus maxima (WTMM). This is because the irregular sampling nature of the WTMM complicates the reconstruction process. Moreover, examples were found to show that the WTMM representation cannot uniquely characterize a signal. It implies that the reconstruction of signal from its WTMM may not be consistently stable. Furthermore, the WTMM approach requires much more computational effort. Therefore, we use the WTMS approach to estimate the regularity of images from the separable wavelet transformed coefficients. Since we do not concern about the localization issue, we allow the decimation to occur when we evaluate the interscale evolution. After the regularity is estimated, this information is utilized in our proposed adaptive regularity scalable wavelet image coding algorithm. This algorithm can be simply embedded into any wavelet image coders, so it is compatible with the existing scalable coding techniques, such as the resolution scalable and signal-to-noise ratio (SNR) scalable coding techniques, without changing the bitstream format, but provides more scalable levels with higher peak signal-to-noise ratios (PSNRs) and lower bit rates. In comparison to the other feature-based wavelet scalable coding algorithms, the proposed algorithm outperforms them in terms of visual perception, computational complexity and coding efficiency.

Optimized Multiple Description Lattice Vector Quantization for Wavelet Image Coding

Multiple description (MD) coding is a promising alternative for robust transmission of information over non-prioritized and unpredictable networks. In this paper, an effective MD image coding scheme is introduced based on the MD lattice vector quantization (MDLVQ) for the wavelet transformed images. In view of the characteristics of wavelet coefficients in different frequency subbands, MDLVQ is applied in an optimized way, including an appropriate construction of wavelet coefficient vectors, the optimization of MDLVQ encoding parameters such as the choice of sublattice index values and the quantization accuracy for different subbands. More importantly, optimized side decoding is employed to predict lost information based on inter-vector correlation and an alternative transmission way for further reducing side distortion. Experimental results validate the effectiveness of the proposed scheme with better performance than some other tested MD image codecs including that based on optimized MD scalar quantization.

Mean shift based log-Gabor wavelet image coding

In this paper, we propose a sparse overcomplete image approximation method based on the ideas of overcomplete log-Gabor wavelet, mean shift and energy concentration. The proposed approximation method selects the necessary wavelet coefficients with a mean shift based algorithm, and concentrates energy on the selected coefficients. It can sparsely approximate the original image, and converges faster than the existing local competition based method. Then, we propose a new compression scheme based on the above approximation method. The scheme has compression performance similar to JPEG 2000. The images decoded with the proposed compression scheme appear more pleasant to the human eyes than those with JPEG 2000.

Adaptive zero-tree structure for curved wavelet image coding

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.

Prediction and adaptive scanning in block-set wavelet image coder

Conditional probability to predict sub-block significance improves the peak signal-to-noise ratio (PSNR) performance of a block-set wavelet image coder. Adaptive scanning of sub-blocks results in a further gain in PSNR performance. Together, the related algorithmic changes introduced by the authors result in competitive PSNR performance without the need for an arithmetic coding stage. As a consequence, timing tests reveal that the critical decoder computational complexity is reduced.

A Progressive Image Compression Method Based on EZW Algorithm

A simple method based on the EZW algorithm is presented for improving image compression performance. Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's EZW(Embedded Zerotree Wavelets)(1), Said and Pearlman's SPIHT(Set Partitioning In Hierarchical Trees)(2), and Bing-Bing Chai's SLCCA(Significance-Linked Connected Component Analysis for Wavelet Image Coding)(3). The EZW algorithm is based on five key concepts: (1) a DWT(Discrete Wavelet Transform) or hierarchical subband decomposition, (2) prediction of the absence of significant information across scales by exploiting self-similarity inherent in images, (3) entropy-coded successive-approximation quantization, (4) universal lossless data compression which is achieved via adaptive arithmetic coding. and (5) DWT coefficients' degeneration from high scale subbands to low scale subbands. In this paper, we have improved the self-similarity statistical characteristic in concept (5) and present a progressive image compression method.

An adaptive edge model in the wavelet domain for wavelet image coding

State-of-art wavelet coders owe their performance to smart ideas for exploiting inter and intra-band dependencies of wavelet coefficients. We claim that developing more efficient coders requires us to look at the main source of these dependencies; i.e., highly localized information around edges. This paper investigates the structural relationships among wavelet coefficients based on an idealized view of edge behavior, and proposes a simple edge model that explains the roots of existing dependencies. We describe how the model is used to approximate and estimate the significant wavelet coefficients. Simulations support its relevance for understanding and analyzing edge information. Specifically, model-based estimation within the space-frequency quantization (SFQ) framework increases the peak signal-to-noise ratio (PSNR) by up to 0.3 dB over the original SFQ coding algorithm. Despite being simple, the model provides valuable insights into the problem of edge-based adaptive modeling of value and location information in the wavelet domain.

An embedded wavelet image coder with parallel encoding and sequential decoding of bit-planes

Wavelet-based coders are widely used in image compression. Many popular embedded wavelet coders are based on a data structure known as zerotree. However, there exists another category of embedded wavelet coders that are fast and efficient without employing zerotree. These coders are based on three key concepts: (1) wavelet coefficient reordering, (2) bit-plane partitioning, and (3) encoding of bit-planes with certain efficient variants of run-length coding. In this paper, we propose a novel method to construct a bit-plane encoder that can be used in this category of non-zerotree coders. Instead of encoding the bit-planes progressively, the bit-plane encoding process can be finished in one pass when multiple bit-plane encoders are activated concurrently. With this proposed method, traditional partitioned-block based parallel processing strategy is enhanced with another dimension (depth of bit-planes) of processing flexibility. This bit-plane encoder inherently targets parallel processing architecture. The final output bitstream can be compatible with that of the original sequential coder if compatibility is preferred over speed and memory efficiency.

Comparative analysis of bitplane-based wavelet image coders

Wavelet image encoders based on bitplanes give excellent results in the compression of still images. The bitplane concept has recently been generalised to vectors, and wavelet image encoders based on vector bitplanes have been proposed, some achieving slightly better rate × distortion performances than scalar encoders. There remains the open question of whether the use of vector bitplanes has the potential of providing more significant rate × distortion improvements over scalar versions. The authors address this question by analysing in detail the performance of adaptations, for the use of vector bitplanes, of four popular wavelet-based bitplane encoders. From this analysis, they determine where the gains in performance of encoders based on vector bitplanes come from. It is concluded that performance improvements may come by increasing the vector dimension, provided that codebooks with good packing properties are used.