Wavelet-based Image Coding Research Articles

Wavelet Based Image Coding via Image Component Prediction Using Neural Networks

In the process of wavelet based image coding, it is possible to enhance the performance by applying prediction. However, it is difficult to apply the prediction using a decoded image to the 2D DWT which is used in JPEG2000 because the decoded pixels are apart from pixels which should be predicted. Therefore, not images but DWT coefficients have been predicted. To solve this problem, predictive coding is applied for one-dimensional transform part in 2D DWT. Zhou and Yamashita proposed to use half-pixel line segment matching for the prediction of wavelet based image coding with prediction. In this research, convolutional neural networks are used as the predictor which estimates a pair of target pixels from the values of pixels which have already been decoded and adjacent to the target row. It helps to reduce the redundancy by sending the error between the real value and its predicted value. We also show its advantage by experimental results.

Low‐complexity block tree image coder for visual sensor networks

The wavelet block tree coding (WBTC) algorithm is an efficient wavelet-based image coder. In this coder, multiple spatial orientation trees are combined together to make a single block tree. It uses three ordered lists to keep track of significant/insignificant coefficients and sets while coding, which increases its memory requirement as well as computational complexity. Also, it uses memory inefficient conventional discrete wavelet transform (DWT) to compute the transformed coefficients. In this study, a Low-Complexity Block Tree Coding (LCBTC) algorithm that uses two state-tables and two very small lists, is proposed. Similar to WBTC, it also uses sorting and refinement passes in each bit-plane. However, it encodes the coefficients in block-tree manner using depth-first search approach to reduce the computational complexity. It uses DWT coefficients obtained from modified fractional wavelet filter (MFrWF) rather than conventional DWT, which further reduces the overall memory and complexity of the image coder. The simulation results show that the memory requirement and computational complexity of LCBTC is much less than WBTC and other state-of-the-art coding algorithms. These features make the image coder a better candidate for compression in memory constrained and real-time visual sensor networks.

Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images

In this paper, a novel method for generation of multiple description (MD) wavelet based image coding is proposed by using Multi-Objective Evolutionary Algorithms (MOEAs). Complexity of the multimedia transmission problem has been increased for MD coders if an input image is affected by any type of noise. In this case, it is necessary to solve two different problems which are designing the optimal side quantizers and estimating optimal parameters of the denoising filter. Existing MD coding (MDC) generation methods are capable of solving only one problem which is to design side quantizers from the given noise-free image but they can fail reducing any type of noise on the descriptions if they applied to the given noisy image and this will cause bad quality of multimedia transmission in networks. Proposed method is used to overcome these difficulties to provide effective multimedia transmission in lossy networks. To achieve it, Dual Tree-Complex Wavelet Transform (DT-CWT) is first applied to the noisy image to obtain the subbands or set of coefficients which are used as a search space in the optimization problem. After that, two different objective functions are simultaneously employed in the MOEA to find pareto optimal solutions with the minimum costs by evolving the initial individuals through generations. Thus, optimal quantizers are created for MDCs generation and obtained optimum parameters are used in the image filter to remove the mixed Gaussian impulse noise on the descriptions effectively. The results demonstrate that proposed method is robust to the mixed Gaussian impulse noise, and offers a significant improvement of optimal side quantizers for balanced MDCs generation at different bitrates.

An improved wavelet-based image coder for embedded greyscale and colour image compression

The embedded zero-tree wavelet (EZW) coding algorithm is a very effective technique for low bitrate still image compression. In this paper, an improved EZW algorithm is proposed to achieve a high compression performance in terms of PSNR and bitrate for lossy and lossless image compression, respectively. To reduce the number of zerotrees, the scanning and symbol redundancy of the existing EZW; the proposed method is based on the use of a new significant symbol map which is represented in a more efficient way. Furthermore, we develop a new EZW-based schemes for achieving a scalable colour image coding by exploiting efficiently the interdependency of colour planes. Numerical results demonstrate a significant superiority of our scheme over the conventional EZW and other improved EZW schemes with respect to both objective and subjective criteria for lossy and lossless compression applications of greyscale and colour images.

Embedded Image Compression: A Review

In this paper, the various research papers related to ‘embedded image compression’ are studied. The aim of the present work is to study the methods adopted and analysis and results obtained. The different methods are adopted by various researchers. The present paper includes discrete wavelet transform (DWT), such as embedded zero wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT) are studied. Block based discrete cosine transform (DCT) encoders are used in many image and video coding standards. Wavelet-based image coders such as embedded zero tree wavelet (EZW) coder, set partitioning in hierarchical trees (SPIHTs), set partitioning embedded block (SPECK), morphological representations of wavelet data (MRWD) and significance-linked connected component analysis (SLCC) are also the part of embedded image compression. Different types of redundancy present in an image, such as Spatial Redundancy, Statistical Redundancy and Human Vision Redundancy are very necessary for analysis. The JPE -2OOU image compression standard is increasingly gaining widespread importance. Ultra spectral imaging is a relatively recent development which makes quantitative remote sensing of the Earth’s surface possible.

ARC 2014

The embedded image processing systems like smartphones and digital cameras have tight limits on storage, computation power, network connectivity, and battery usage. These limitations make it important to ensure efficient image coding. In the article, we present a novel heap-based priority queue structure employed by an Adaptive Scanning of Wavelet Data scheme (ASWD) targeting an embedded platform. ASWD is a context modeling block implemented via priority queues in a wavelet-based image coder to reorganize the wavelet coefficients into locally stationary sequences. The architecture we propose exploits efficient use of FPGA’s on-chip dual-port memories in an adaptive manner. Innovations of index-aware system linked to each element in the queue makes the location of queue element traceable in the heap as per the requirements of the ASWD algorithm. Moreover, use of 4-port memories along with intelligent data concatenation of queue elements yielded in a cost effective enhanced memory access. The memory ports are adaptively assigned to different units during different processing phases in a manner to optimally take advantage of memory access required by that phase. The architectural innovations can also be exploited in other applications that require efficient hardware implementations of generic priority queue or classical sorting applications which sort into the index. We designed and validated the hardware on an Altera’s Stratix IV FPGA as an IP accelerator in a Nios II processor based System on Chip. We show that our architecture at 150MHz can provide 45X speedup compared to an embedded ARM Cortex-A9 processor at 666MHz targeting the throughput of 10MB/s.

FrWF-Based LMBTC: Memory-Efficient Image Coding for Visual Sensors

After the successful development of JPEG2000, many state-of-the-art wavelet-based image coding algorithms have been developed. However, the traditional discrete wavelet transform (DWT) is implemented with memory intensive and time-consuming algorithms and, therefore, has very high system resource requirements. In particular, the very large requirement of memory poses a serious limitation for multimedia applications on memory-constrained portable devices, such as digital cameras and sensor nodes. In this paper, we propose a novel wavelet-based image coder with low memory requirements and low complexity that preserves the compression efficiency. Our encoder employs the fractional wavelet filter (FrWF) to calculate the DWT coefficients, which are quantized and encoded with a novel low memory block tree coding (LMBTC) algorithm. The LMBTC is a listless form of the wavelet block tree coding algorithm. Simulation results demonstrate that the proposed coder significantly reduces memory requirements and computational complexity and has competitive coding efficiency in comparison with other state-of-the-art coders. The FrWF combined with the LMBTC is, thus, a viable option for image communication over wireless sensor networks.

Dynamic Multiple Description Wavelet Based Image Coding Using Enhanced Particle Swarm Optimization

In this paper, dynamic multiple description wavelet based image coding using enhanced particle swarm optimization (EPSO) is proposed for effective multimedia transmission in the dynamically changing network topologies such as mobile ad-hoc networks (MANETs). In wireless networks, multimedia transmission has become a challenging problem because of changing conditions over time such as packet loss rate (PLR), capacity/bandwidth, network and link conditions. The proposed method is used to simultaneously create optimal multiple description coding (MDC) or sub-images based on the dynamically changing capacity/bandwidth of wireless joint/disjoint communication channels/paths. To perform it, PSO is enhanced by adding a position mutation scheme as a generation feature to employ the proposed dynamic cost function to find the optimal solution within in a search space. To create MDs efficiently, three different wavelet transformation methods which are Discrete Wavelet Transform (DWT), Dual Tree-Complex Wavelet Transform (DT-CWT), and Stationary Wavelet Transform (SWT) are investigated. In this manner, a wavelet transform method is first applied to the given input image to create the sub-bands or set of coefficients as a search space, and then EPSO is employed to find optimal results in the search space to generate MDs. The proposed approach produces good quality of descriptions for effective multimedia transmission over dynamically changing and unreliable communication networks, particularly in wireless and ad-hoc networks (MANETs). The results of the proposed method are compared with the state-of-the-art techniques and simulation results demonstrate that using EPSO with DT-CWT provides a significant improvement of optimal and balanced MDC generation with good quality at different bit-rates.

Wavelet based volumetric medical image compression

The amount of image data generated each day in health care is ever increasing, especially in combination with the improved scanning resolutions and the importance of volumetric image data sets. Handling these images raises the requirement for efficient compression, archival and transmission techniques. Currently, JPEG 2000׳s core coding system, defined in Part 1, is the default choice for medical images as it is the DICOM-supported compression technique offering the best available performance for this type of data. Yet, JPEG 2000 provides many options that allow for further improving compression performance for which DICOM offers no guidelines. Moreover, over the last years, various studies seem to indicate that performance improvements in wavelet-based image coding are possible when employing directional transforms. In this paper, we thoroughly investigate techniques allowing for improving the performance of JPEG 2000 for volumetric medical image compression. For this purpose, we make use of a newly developed generic codec framework that supports JPEG 2000 with its volumetric extension (JP3D), various directional wavelet transforms as well as a generic intra-band prediction mode. A thorough objective investigation of the performance-complexity trade-offs offered by these techniques on medical data is carried out. Moreover, we provide a comparison of the presented techniques to H.265/MPEG-H HEVC, which is currently the most state-of-the-art video codec available. Additionally, we present results of a first time study on the subjective visual performance when using the aforementioned techniques. This enables us to provide a set of guidelines and settings on how to optimally compress medical volumetric images at an acceptable complexity level.

Entropy-based evaluation of context models for wavelet-transformed images.

Entropy is a measure of a message uncertainty. Among others aspects, it serves to determine the minimum coding rate that practical systems may attain. This paper defines an entropy-based measure to evaluate context models employed in wavelet-based image coding. The proposed measure is defined considering the mechanisms utilized by modern coding systems. It establishes the maximum performance achievable with each context model. This helps to determine the adequateness of the model under different coding conditions and serves to predict with high precision the coding rate achieved by practical systems. Experimental results evaluate four well-known context models using different types of images, coding rates, and transform strategies. They reveal that, under specific coding conditions, some widely-spread context models may not be as adequate as it is generally thought. The hints provided by this analysis may help to design simpler and more efficient wavelet-based image codecs.

Meta-heuristic algorithms for optimized network flow wavelet-based image coding

Optimal multipath selection to maximize the received multiple description coding (MDCs) in a lossy network model is proposed. Multiple description scalar quantization (MDSQ) has been applied to the wavelet coefficients of a color image to generate the MDCs which are combating transmission loss over lossy networks. In the networks, each received description raises the reconstruction quality of an MDC-coded signal (image, audio or video). In terms of maximizing the received descriptions, a greater number of optimal routings between source and destination must be obtained. The rainbow network flow (RNF) collaborated with effective meta-heuristic algorithms is a good approach to resolve it. Two meta-heuristic algorithms which are genetic algorithm (GA) and particle swarm optimization (PSO) have been utilized to solve the multi-objective optimization routing problem for finding optimal routings each of which is assigned as a distinct color by RNF to maximize the coded descriptions in a network model. By employing a local search based priority encoding method, each individual in GA and particle in PSO is represented as a potential solution. The proposed algorithms are compared with the multipath Dijkstra algorithm (MDA) for both finding optimal paths and providing reliable multimedia communication. The simulations run over various random network topologies and the results show that the PSO algorithm finds optimal routings effectively and maximizes the received MDCs with assistance of RNF, leading to reduce packet loss and increase throughput.

General Embedded Quantization for Wavelet-Based Lossy Image Coding

Embedded quantization is a mechanism employed by many lossy image codecs to progressively refine the distortion of a (transformed) image. Currently, the most common approach to do so in the context of wavelet-based image coding is to couple uniform scalar deadzone quantization (USDQ) with bitplane coding (BPC). USDQ+BPC is convenient for its practicality and has proved to achieve competitive coding performance. But the quantizer established by this scheme does not allow major variations. This paper introduces a multistage quantization scheme named general embedded quantization (GEQ) that provides more flexibility to the quantizer. GEQ schemes can be devised for specific decoding rates achieving optimal coding performance. Practical approaches of GEQ schemes achieve coding performance similar to that of USDQ+BPC while requiring fewer quantization stages. The performance achieved by GEQ is evaluated in this paper through experimental results carried out in the framework of modern image coding systems.

PGBbBShift - Perceptual Generalized Bitplane-by-Bitplane Shift

The paper we present pGBbBShift. This algorithm permits to code any Region of Interest (ROI) in a perceptual way, i.e. the presented algorithm introduces some characteristics of the Human Visual System. Furthermore, it introduces features of chromatic induction to the GBbBShift method when bitplanes of ROI and background areas are coded. Thus, the included features balance visual importance of some pixels regardless their numerical importance, namely we avoid to use Information Theory criteria. Visual criteria are applied using the CIWaM, which is contrast band-pass ?lter that predicts color perception. pGBbBShift is compared against classical ROI algorithms, such as the MaxShift method of JPEG2000 and results show that there is no perceptual difference. pGBbBShift method is an open algorithm that can be applied in any wavelet based image coder such as JPEG2000, SPIHT or SPECK. Finally, we applied pGBbBShift to Hi-SET coder and we obtain the best results when the overall visual image quality is assessed

Multiple Description Wavelet-Based Image Coding Using Iterated Function System

Recent literature highlights the multiple description coding (MDC) as a promising method to solve the problem of resilient image coding over error-prone networks, where packet losses occur. In this paper, we introduce a novel multiple description wavelet-based image coding scheme using fractal. This scheme exploits the fractal’s ability, which is to describe the different resolution scale similarity (redundancy) among wavelet coefficient blocks. When one description is lost, the lost information can be reconstructed by the proposed iterated function system (IFS) recovering scheme with the similarity and some introduced information. Compared with the referenced methods, the experimental results suggest that the proposed scheme can achieve better performance. Furthermore, it is substantiated to be more robust for images transmission and better subjective quality in reconstructed images even with high packet loss ratios.

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.

Wavelet based Image Coding Schemes: A Recent Survey

Wavelet based Image Coding Schemes: A Recent Survey

A Novel Error Resilient Scheme for Wavelet-based Image Coding Over Packet Networks

this paper presents a robust transmission strategy for wavelet based scalable bit stream over packet erasure channel. By taking the advantage of the bit plane coding and the multiple description coding, the proposed strategy adopts layered multiple description coding (LMDC) for the embedded wavelet coders to improve the error resistant capability of the important bit planes in the meaning of D(R) function. Then, the post-compression rate-distortion (PCRD) optimization process is used to improve the overall performance. At decoding, a simple error concealment technique is used to compensate the data loss. The proposed method is applied on Set Partitioning in Hierarchical Trees (SPIHT) coded image and the experimental results show graceful degradation for variable packet erasure rate.

Rate Control Algorithms for Non-Embedded Wavelet-Based Image Coding

During the last decade, there has been an increasing interest in the design of very fast wavelet image encoders focused on specific applications like interactive real-time image and video systems, running on power-constrained devices such as digital cameras, mobile phones where coding delay and/or available computing resources (working memory and power processing) are critical for proper operation. In order to reduce complexity, most of these fast wavelet image encoders are non-(SNR)-embedded and as a consequence, precise rate control is not supported. In this work, we propose some simple rate control algorithms for these kind of encoders and we analyze their impact to determine if, despite their inclusion, the global encoder is still competitive with respect to popular embedded encoders like SPIHT and JPEG2000. In this study we focus on the non-embedded LTW encoder, showing that the increase in complexity due to the rate control algorithm inclusion, maintains LTW competitive with respect to SPIHT and JPEG2000 in terms of R/D performance, coding delay and memory consumption.

Still Image Compression by Combining EZW Encoding with Huffman Encoder

Image compression can improve the performance of the digital systems by reducing time and cost in image storage and transmission without significant reduction of the image quality. For image compression it is desirable that the selection of transform should reduce the size of resultant data set as compared to source data set. EZW is computationally very fast and among the best image compression algorithm known today. This paper proposes a technique for image compression which uses the Wavelet-based Image Coding in combination with Huffman encoder for further compression. A large number of experimental results are shown that this method saves a lot of bits in transmission, further enhances the compression performance. This paper aims to determine the best threshold to compress the still image at a particular decomposition level by combining the EZW encoder with Huffman Encoder. Compression Ratio (CR) and Peak-Signalto-Noise (PSNR) is determined for different threshold values ranging from 6 to 60 for decomposition level 8.

A WAVELET-BASED PERCEPTUAL IMAGE CODER INCORPORATING A NEW MODEL FOR COMPRESSION OF COLOR IMAGES

A wavelet-based perceptual image coder for the compression of color images is proposed here in which the coding structure is coupled with Human Visual System models to produce high quality images. The major contribution is the development of a new model for the compression of the color components based on psychovisual experiments, which quantifies the optimum amount of compression that can be applied to the color components for a given rate. The model is developed for YCbCr color space and the perceptually uniform CIE Lab color space. A complete coding structure for the compression of color images is developed by incorporating the new perceptual model. The performance of the proposed coder is compared with a wavelet-based coder that uses the quantization stage of the JPEG2000 standard. The perceptual quality of the compressed images is tested using the wavelet-based subjective and objective perceptual quality matrices such as Mean Opinion Score, Visual Information Fidelity and Visual Signal to Noise Ratio. Though the model is developed for a perceptually lossless high quality image compression, results obtained reveal that the proposed structure gives very good perceptual quality compared to the existing schemes for lower bit rates. These advantages make the proposed coder a candidate for replacing the encoder stage of the current image compression standards.