Image Compression Performance Research Articles

Selection of Wavelet Basis Function for Image Compression – A Review

Wavelets are being suggested as a platform for various tasks in image processing. The advantage of wavelets lie in its time frequency resolution. The use of different basis functions in the form of different wavelets made the wavelet analysis as a destination for many applications. The performance of a particular technique depends on the wavelet coefficients arrived after applying the wavelet transform. The coefficients for a specific input signal depends on the basis functions used in the wavelet transform. Hence in this paper toward this end, different basis functions and their features are presented. As the image compression task depends on wavelet transform to large extent from few decades, the selection of basis function for image compression should be taken with care. In this paper, the factors influencing the performance of image compression are presented.

Design and implementation of a novel low complexity symmetric orthogonal wavelet filter‐bank

The design of wavelet filter-bank in real continuum domain demands infinite precision to achieve perfect reconstruction (PR). The computations with irrational coefficients in orthogonal filters require a large amount of hardware that result in increased power dissipation, huge memory requirements and reduction in speed of operation. This paper presents a new approach to obtain a complete dyadic (low complexity) 6-tap orthogonal symmetric wavelet filter-bank (FB) with near perfect reconstruction. This is achieved by slightly altering the PR conditions to make orthogonal filters symmetric and to obtain complete dyadic filter coefficients. The proposed wavelet FB has reduced dynamic power dissipation and significantly reduced adder and shifter count. The VLSI architecture of the proposed FB is designed and implemented on Kintex-7 FPGA. The architectural design is carried out using Verilog and functional simulation and synthesis are executed using Vivado 2016.4 software. The proposed FB dissipates 114mW dynamic power and requires only 5 adders and 3 shifters. The effectiveness of the proposed wavelet is verified in three different applications namely image compression, iris recognition system and orthogonal frequency division multiplexing (OFDM). The proposed wavelet FB gives comparable performance in image compression and attains superior performance in iris recognition system (feature extraction) and OFDM.

Rate-Distortion Optimized Sparse Coding With Ordered Dictionary for Image Set Compression

Image set compression has recently emerged as an active research topic due to the rapidly increasing demand in cloud storage. In this paper, we propose a novel framework for image set compression based on the rate-distortion optimized sparse coding. Specifically, given a set of similar images, one representative image is first identified according to the similarity among these images, and a dictionary can be learned subsequently in wavelet domain from the training samples collected from the representative image. In order to improve coding efficiency, the dictionary atoms are reordered according to their use frequencies when representing the representative image. As such, the remaining images can be efficiently compressed with sparse coding based on the reordered dictionary that is highly adaptive to the content of the image set. To further improve the efficiency of sparse coding, the number of dictionary atoms for image patches is further optimized in a rate-distortion sense. Experimental results show that the proposed method can significantly improve the image compression performance compared with JPEG, JPEG2000, and the state-of-the-art dictionary learning-based methods.

Hybrid image denoising method based on non‐subsampled contourlet transform and bandelet transform

The second generation bandelet transform uses the two-dimensional (2D) separable wavelet transform to improve its image denoising and compression performance. However, the 2D separable wavelet transform is not a shift-invariant transform and therefore cannot capture geometric information well. The authors propose a hybrid image denoising method in which the 2D separable wavelet transform in the second generation bandelet transform is replaced with the non-subsampled contourlet transform. The results of the application of the proposed method to several greyscale and colour benchmark images contaminated with various levels of Gaussian white noise and Poisson noise indicate that the proposed method has good peak signal-to-noise ratio and visual quality performance.

An FPGA Implementation of a Convolutional Auto-Encoder

In order to simplify the hardware design and reduce the resource requirements, this paper proposes a novel implementation of a convolutional auto-encoder (CAE) in a field programmable gate array (FPGA). Instead of the traditional framework realized in a layer-by-layer way, we designed a new periodic layer-multiplexing framework for CAE. Only one layer is introduced and periodically reused to establish the network, which consumes fewer hardware resources. Moreover, by fixing the number of channels, this framework can be applicable to an image of arbitrary size. Furthermore, to effectively improve the speed of convolution calculation, the parallel convolution method is used based on shift registers. Experimental results show that the proposed CAE framework achieves good performance in image compression. It can be observed that our CAE framework has advantages in resources occupation, operation speed, and power consumption, indicating great potential for application in digital signal processing.

Closed-Form Optimization on Saliency-Guided Image Compression for HEVC-MSP

High efficiency video coding (HEVC) is the latest video coding standard, and it has the best performance among all the existing standards. HEVC main still picture profile (HEVC-MSP) also achieves top performance in image compr-ession. In this paper, we propose a closed-form bit allocation approach to optimize the saliency-guided PSNR (viewed as perceptual distortion) such that the coding efficiency of HEVC-based image compression can be significantly improved from a subjective perspective. Specifically, a bit allocation formulation is established to minimize perceptual distortion with a constraint on bit-rates. Then, this formulation is solved using the proposed recursive Taylor expansion method with a closed-form solution. On the basis of our solution, a bit allocation and re-allocation process is developed in our approach to minimize perceptual distortion, meanwhile accurately controlling bit-rates. In addition, we provide both theoretical and numerical analyses of the computational complexity, verifying the little extra time cost of our approach. The experimental results demonstrate the superior performance of our approach over the state-of-the-art HEVC-MSP, and the BD-rate savings are approximately 40% and 24% for face and generic images, respectively.

Novel Image Set Compression Algorithm Using Rate-Distortion Optimized Multiple Reference Image Selection

Image set compression has recently become an active research topic due to the explosion of digital photographs. In order to efficiently compress the image sets of similar images with moving objects, in this paper, we propose a novel algorithm for image set compression using multiple reference images. First, for an image set, its depth-constrained minimum arborescence is generated. We then present a reference image candidate determination method to build the reference image candidates for the images of the set. Furthermore, we propose a rate-distortion optimized multiple reference image selection method. This method compares the correlation between every image and each of its reference image candidates to produce its multiple reference images. Finally, compressed image data are achieved by employing block-based motion compensation and residue coding. In addition, we also give a new way of access to images to keep the same access delay with single reference image-based schemes. Compared with the state-of-the-art image compression algorithms, experimental results show that our proposed algorithm can significantly improve the image compression performance.

Quantization based wavelet transformation technique for digital image compression with removal of multiple artifacts and noises

A Morlet’s wavelet transformation based image compression and decompression (MWT-ICD) technique is proposed in order to enhance the performance of digital and gray scale image compression with higher compression ratio (CR) and to reduce the space complexity. The MWT-ICD technique initially performs preprocessing task to remove multiple artifacts and noises in digital and gray scale images with the application of generalized lapped orthogonal transforms and Wiener filter. This process results in improved quality of digital and gray scale images with higher PSNR for compression. Next, wavelet quantization transformation based image compression algorithm is developed in MWT-ICD Technique using Morlet’s wavelet transformation. Finally, quantized wavelet transformation based image decompression process is carried out in MWT-ICD technique with the objective of obtaining the reconstructed original image. The performance of MWT-ICD technique is measured in terms of CR, compression time (CT), and space complexity (SC), peak signal to noise ratio (PSNR) and compared with four existing methods. The experimental results show that the MWT-ICD technique is able to acquire higher CR and also reduced space complexity when compared to existing DCT-based image compression system based on Laplacian transparent composite model and multi-wavelet based compressed sensing technique

Hybrid WPT‐BDCT transform for high‐quality image compression

The image compression performance of transform coders highly depends on the energy compaction (EC) capability of transforms. The popular transforms such as discrete cosine transform (DCT) and discrete wavelet transform (DWT) provide decent EC; however, their capability is not enough to deliver good quality image reconstruction for higher compression levels (CLs). In this study, the authors propose a new hybrid transform which is a fusion of wavelet packet transform (WPT) and block-DCT (BDCT) transform to achieve high-quality image compression. This new hybrid WPT-BDCT transform is able to attain higher EC than the existing transforms. Further, the authors have found a new energy compaction index (ECI) to evaluate the energy compactness of the image transforms. The proposed hybrid transform has been extensively evaluated, based on proposed ECI parameter, the visual quality assessment of reconstructed images and with the standard image quality indexes peak-signal-to-noise ratio and structural similarity index measure. It is reported that the proposed hybrid transform provides higher EC and outperforms the transforms namely DCT, DWT, WPT, multi-wavelet transform and existing hybrid transforms for all the CLs.

A Cost and Power Efficient Image Compressor VLSI Design With Fuzzy Decision and Block Partition for Wireless Sensor Networks

This paper presents a novel hardware-oriented image compression algorithm and its very large-scale integration (VLSI) implementation for wireless sensor networks. The proposed novel image compression algorithm consists of a fuzzy decision, block partition, digital halftoning, and block truncation coding (BTC) techniques. A novel variable-size block partition technique was used in the proposed algorithm to improve image quality and compression performance. In addition, eight different types of blocks were encoded by Huffman coding according to probability to increase the compression ratio further. In order to achieve the low-cost and low-power characteristics, a novel iteration-based BTC training module was created to get representative levels and meet the requirement of wireless sensor networks. A prediction and a modified Golomb–Rice coding modules were designed to encode the information of representative levels to achieve higher compression performance. The proposed algorithm was realized by a VLSI technique with an UMC 0.18- $\mu \text{m}$ CMOS process. The synthesized gate counts and core area of this design were 6.4 k gate counts and 60 000 $\mu \text{m}^{2}$ , respectively. The operating frequency and power consumption were 100 MHz and 3.11 mW respectively. Compared with previous JPEG, JPEG-LS, and fixed-size BTC-based designs, this work reduced 20.9% gate counts more than previous designs. Moreover, the proposed design required only a one-line-buffer memory rather than a frame-buffer memory required by previous designs.

GPU Implementation of Bitplane Coding with Parallel Coefficient Processing for High Performance Image Compression

The fast compression of images is a requisite in many applications like TV production, teleconferencing, or digital cinema. Many of the algorithms employed in current image compression standards are inherently sequential. High performance implementations of such algorithms often require specialized hardware like field integrated gate arrays. Graphics Processing Units (GPUs) do not commonly achieve high performance on these algorithms because they do not exhibit fine-grain parallelism. Our previous work introduced a new core algorithm for wavelet-based image coding systems. It is tailored for massive parallel architectures. It is called bitplane coding with parallel coefficient processing (BPC-PaCo). This paper introduces the first high performance, GPU-based implementation of BPC-PaCo. A detailed analysis of the algorithm aids its implementation in the GPU. The main insights behind the proposed codec are an efficient thread-to-data mapping, a smart memory management, and the use of efficient cooperation mechanisms to enable inter-thread communication. Experimental results indicate that the proposed implementation matches the requirements for high resolution (4 K) digital cinema in real time, yielding speedups of 30 $\times$ with respect to the fastest implementations of current compression standards. Also, a power consumption evaluation shows that our implementation consumes 40 $\times$ less energy for equivalent performance than state-of-the-art methods.

Multimedia transmission in MC-CDMA using adaptive subcarrier power allocation and CFO compensation

ABSTRACTMulticarrier code division multiple access (MC-CDMA) system is one of the most effective techniques in fourth-generation (4G) wireless technology, due to its high data rate, high spectral efficiency and resistance to multipath fading. However, MC-CDMA systems are greatly deteriorated by carrier frequency offset (CFO) which is due to Doppler shift and oscillator instabilities. It leads to loss of orthogonality among the subcarriers and causes intercarrier interference (ICI). Water filling algorithm (WFA) is an efficient resource allocation algorithm to solve the power utilisation problems among the subcarriers in time-dispersive channels. The conventional WFA fails to consider the effect of CFO. To perform subcarrier power allocation with reduced CFO and to improve the capacity of MC-CDMA system, residual CFO compensated adaptive subcarrier power allocation algorithm is proposed in this paper. The proposed technique allocates power only to subcarriers with high channel to noise power ratio. The performance of the proposed method is evaluated using random binary data and image as source inputs. Simulation results depict that the bit error rate performance and ICI reduction capability of the proposed modified WFA offered superior performance in both power allocation and image compression for high-quality multimedia transmission in the presence of CFO and imperfect channel state information conditions.

A Survey on Image Compression using Burrows Wheeler Transform

<em>This paper is a methodological review paper on image compression using Burrows Wheeler Transform. Normally BWT is used for text compression but it has been recently applied to image compression field. Basically it is a lossless compression technique which is used for high level resolution.This paper proposes about several scheme added with BWT to improve the performance of image compression which helps us to formulate a new technique for the further improvement in BWT. Here many authorshave different type of representation of BWT for better compression.</em>

A Survey on Image Compression using Burrows Wheeler Transform

<em>This paper is a methodological review paper on image compression using Burrows Wheeler Transform. Normally BWT is used for text compression but it has been recently applied to image compression field. Basically it is a lossless compression technique which is used for high level resolution.This paper proposes about several scheme added with BWT to improve the performance of image compression which helps us to formulate a new technique for the further improvement in BWT. Here many authorshave different type of representation of BWT for better compression.</em>

Performance analysis of Horner's rule-based canonical signed digit lifting architecture for two-dimensional discrete wavelet transform

The high performance of image compression is needed greater as digital image finds its way into many areas of everyday life. JPEG 2000 is one of the best image compression standards in which DWT (Discrete Wavelet Transform) is used. In this paper, an efficient two-dimensional (2D) DWT 9/7 and 6/10 filter based on Horner's Rule with Canonical Signed Digit is proposed. Canonical Signed Digit is based on the positional values of 1's in the representation, whereas Horner's rule-based Canonical Signed Digit is based on the distance between the positions of 1's. Implementing the Canonical Signed Digit and Horner's Rule with Canonical Signed Digit architectures of 9/7 and 6/10 filters in Synopsys IC Compiler with 90 nm technology shows the reduction of 31.07% and 36.86% in the total dynamic power along with reduction of 25.72% and 31.17% in the total area, respectively, when compared with the existing method.

Synthetic aperture radar image compression based on multi-scale geometric transforms

Image representation in separable orthogonal basis cannot take advantage of geometrical regularity contained in basic images. When, explored efficiently geometrical regularity improves image compression. In this paper, we propose to experiment and compare an adaptive multi-scale geometric decomposition for synthetic aperture radar (SAR) image compression, called multi-scale bandelet transform, and a non-adaptive multi-scale geometric representation called ridgelet transform. The second generation of bandelet transform adopted in this work, is constructed in discrete domain with bandeletisation of warped wavelet transform along the optimal direction of geometric flow that minimises the Lagrangian. We discuss the criteria and results to assess SAR image compression performances using wavelet, bandelet, and ridgelet transforms. Our experiments revealed that during the compression phase, the speckle noise is removed from the SAR images inducing further improvements of the coding efficiency. In order, to evaluate the robustness of bandelet transform, we have proposed a progressive compression scheme based on the second generation of bandelet transform combined to SPIHT encoder, which is generally integrated with the wavelet transform.

Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding.

A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times the data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing “original pixel intensity”-based coding approaches using traditional image coders (e.g., JPEG2000) to the “residual”-based approaches using a video coder for better compression performance. A modified video coder is required to exploit spatial-spectral redundancy using pixel-level reflectance modelling due to the different characteristics of HS images in their spectral and shape domain of panchromatic imagery compared to traditional videos. In this paper a novel coding framework using Reflectance Prediction Modelling (RPM) in the latest video coding standard High Efficiency Video Coding (HEVC) for HS images is proposed. An HS image presents a wealth of data where every pixel is considered a vector for different spectral bands. By quantitative comparison and analysis of pixel vector distribution along spectral bands, we conclude that modelling can predict the distribution and correlation of the pixel vectors for different bands. To exploit distribution of the known pixel vector, we estimate a predicted current spectral band from the previous bands using Gaussian mixture-based modelling. The predicted band is used as the additional reference band together with the immediate previous band when we apply the HEVC. Every spectral band of an HS image is treated like it is an individual frame of a video. In this paper, we compare the proposed method with mainstream encoders. The experimental results are fully justified by three types of HS dataset with different wavelength ranges. The proposed method outperforms the existing mainstream HS encoders in terms of rate-distortion performance of HS image compression.

STUDY AND PERFORMANCE OF EZW AND SPIHT ALGORITHM IN SATELLITE IMAGE COMPRESSION.

29May 2016 STUDY AND PERFORMANCE OF EZW AND SPIHT ALGORITHM IN SATELLITE IMAGE COMPRESSION. Boussaha Abdelkader and Djebbouri Mohamed. Department of Electronic, Djilali Liabes University, Sidi Bel Abbes, Algeria. Department of Electronic, Djilali Liabes University, Sidi Bel Abbes, Algeria.

Image Compression: A Comparative Study between ANN and Traditional Approach

compression is a highly essential part of image processing and is a necessity of the modern world required in various fields. It is a process of representing image data using fewer bits than it is required for the original, by performing image compression a certain amount of data used by the image for its storage can be reduced. Compression is necessary in cases where a large amount of data is to be stored or transferred. This paper reviews some of the conventional methods for achieving Image compression, viz. Run length encoding, DCT, DWT to name a few. Artificial neural networks can also be used to achieve image compression. Here, an attempt is made to compare between the traditional methods of performing image compression and the artificial neural network approach. KeywordsCompression, Run-length encoding, DCT, DWT, Levenberg-Marquardt.

Lossless compression of hyperspectral images using conventional recursive least-squares predictor with adaptive prediction bands

An efficient lossless compression scheme for hyperspectral images using conventional recursive least-squares (CRLS) predictor with adaptive prediction bands is proposed. The proposed scheme first calculates the preliminary estimates to form the input vector of the CRLS predictor. Then the number of bands used in prediction is adaptively selected by an exhaustive search for the number that minimizes the prediction residual. Finally, after prediction, the prediction residuals are sent to an adaptive arithmetic coder. Experiments on the newer airborne visible/infrared imaging spectrometer (AVIRIS) images in the consultative committee for space data systems (CCSDS) test set show that the proposed scheme yields an average compression performance of 3.29 (bits/pixel) , 5.57 (bits/pixel) , and 2.44 (bits/pixel) on the 16-bit calibrated images, the 16-bit uncalibrated images, and the 12-bit uncalibrated images, respectively. Experimental results demonstrate that the proposed scheme obtains compression results very close to clustered differential pulse code modulation-with-adaptive-prediction-length, which achieves best lossless compression performance for AVIRIS images in the CCSDS test set, and outperforms other current state-of-the-art schemes with relatively low computation complexity.