Showing papers on "Quantization (image processing) published in 2014"

Hybrid No-Reference Quality Metric for Singly and Multiply Distorted Images

Abstract: In a typical image communication system, the visual signal presented to the end users may undergo the steps of acquisition, compression and transmission which cause the artifacts of blurring, quantization and noise. However, the researches of image quality assessment (IQA) with multiple distortion types are very limited. In this paper, we first introduce a new multiply distorted image database (MDID2013), which is composed of 324 images that are simultaneously corrupted by blurring, JPEG compression and noise injection. We then propose a new six-step blind metric (SISBLIM) for quality assessment of both singly and multiply distorted images. Inspired by the early human visual model and recently revealed free energy based brain theory, our method works to systematically combine the single quality prediction of each emerging distortion type and joint effects of different distortion sources. Comparative studies of the proposed SISBLIM with popular full-reference IQA approaches and start-of-the-art no-reference IQA metrics are conducted on five singly distorted image databases (LIVE, TID2008, CSIQ, IVC, Toyama) and two newly released multiply distorted image databases (LIVEMD, MDID2013). Experimental results confirm the effectiveness of our blind technique. MATLAB codes of the proposed SISBLIM algorithm and MDID2013 database will be available online at http://gvsp.sjtu.edu.cn/.

Packing and Padding: Coupled Multi-index for Accurate Image Retrieval

Abstract: In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low discriminative power, so false positive matches occur prevalently. Apart from the information loss during quantization, another cause is that the SIFT feature only describes the local gradient distribution. To address this problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform feature fusion at indexing level. Basically, complementary features are coupled into a multi-dimensional inverted index. Each dimension of c-MI corresponds to one kind of feature, and the retrieval process votes for images similar in both SIFT and other feature spaces. Specifically, we exploit the fusion of local color feature into c-MI. While the precision of visual match is greatly enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation of SIFT and color features significantly reduces the impact of false positive matches. Extensive experiments on several benchmark datasets demonstrate that c-MI improves the retrieval accuracy significantly, while consuming only half of the query time compared to the baseline. Importantly, we show that c-MI is well complementary to many prior techniques. Assembling these methods, we have obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench datasets, respectively, which compare favorably with the state-of-the-arts.

Packing and Padding: Coupled Multi-index for Accurate Image Retrieval

Abstract: In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low discriminative power, so false positive matches occur prevalently. Apart from the information loss during quantization, another cause is that the SIFT feature only describes the local gradient distribution. To address this problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform feature fusion at indexing level. Basically, complementary features are coupled into a multi-dimensional inverted index. Each dimension of c-MI corresponds to one kind of feature, and the retrieval process votes for images similar in both SIFT and other feature spaces. Specifically, we exploit the fusion of local color feature into c-MI. While the precision of visual match is greatly enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation of SIFT and color features significantly reduces the impact of false positive matches. Extensive experiments on several benchmark datasets demonstrate that c-MI improves the retrieval accuracy significantly, while consuming only half of the query time compared to the baseline. Importantly, we show that c-MI is well complementary to many prior techniques. Assembling these methods, we have obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench datasets, respectively, which compare favorably with the state-of-the-arts.

Reversible watermarking scheme for medical image based on differential evolution

Abstract: A reversible watermarking method is proposed with wavelet transforms and SVD.Signature and logo data are inserted by recursive dither modulation algorithm.DE is explored to design the quantization steps optimally.Good balance of imperceptibility, robustness and capacity is obtained by DE.Experiments show good performance and outperform the related algorithms. Currently, most medical images are stored and exchanged with little or no security; hence it is important to provide protection for the intellectual property of these images in a secured environment. In this paper, a new and reversible watermarking method is proposed to address this security issue. Specifically, signature information and textual data are inserted into the original medical images based on recursive dither modulation (RDM) algorithm after wavelet transform and singular value decomposition (SVD). In addition, differential evolution (DE) is applied to design the quantization steps (QSs) optimally for controlling the strength of the watermark. Using these specially designed hybrid techniques, the proposed watermarking technique obtains good imperceptibility and high robustness. Experimental results indicate that the proposed method is not only highly competitive, but also outperforms the existing methods.

No-Reference Quality Assessment of JPEG Images via a Quality Relevance Map

Abstract: This letter presents a no-reference quality assessment algorithm for JPEG compressed images (NJQA). Our method does not specifically aim to measure blockiness. Instead, quality is estimated by first counting the number of zero-valued DCT coefficients within each block, and then using a map, which we call the quality relevance map, to weight these counts. The quality relevance map for an image is a map that indicates which blocks are naturally uniform (or near-uniform) vs. which blocks have been made uniform (or near-uniform) via JPEG compression. Testing on various image-quality databases demonstrates that NJQA is either competitive with or outperforms modern competing methods on JPEG images.

Jpeg Image Compression Using Discrete Cosine Transform - A Survey

Abstract: Due to the increasing requirements for transmission of images in computer, mobile environments, the research in the field of image compression has increased significantly. Image compression plays a crucial role in digital image processing, it is also very important for efficient transmission and storage of images. When we compute the number of bits per image resulting from typical sampling rates and quantization methods, we find that Image compression is needed. Therefore development of efficient techniques for image compression has become necessary .This paper is a survey for lossy image compression using Discrete Cosine Transform, it covers JPEG compression algorithm which is used for full-colour still image applications and describes all the components of it.

An Effective Method for Detecting Double JPEG Compression With the Same Quantization Matrix

Abstract: Detection of double JPEG compression plays an important role in digital image forensics. Some successful approaches have been proposed to detect double JPEG compression when the primary and secondary compressions have different quantization matrices. However, detecting double JPEG compression with the same quantization matrix is still a challenging problem. In this paper, an effective error-based statistical feature extraction scheme is presented to solve this problem. First, a given JPEG file is decompressed to form a reconstructed image. An error image is obtained by computing the differences between the inverse discrete cosine transform coefficients and pixel values in the reconstructed image. Two classes of blocks in the error image, namely, rounding error block and truncation error block, are analyzed. Then, a set of features is proposed to characterize the statistical differences of the error blocks between single and double JPEG compressions. Finally, the support vector machine classifier is employed to identify whether a given JPEG image is doubly compressed or not. Experimental results on three image databases with various quality factors have demonstrated that the proposed method can significantly outperform the state-of-the-art method.

Jpeg Image Compression Using Discrete Cosine Transform - A Survey

Abstract: Due to the increasing requirements for transmission of images in computer, mobile environments, the research in the field of image compression has increased significantly. Image compression plays a crucial role in digital image processing, it is also very important for efficient transmission and storage of images. When we compute the number of bits per image resulting from typical sampling rates and quantization methods, we find that Image compression is needed. Therefore development of efficient techniques for image compression has become necessary .This paper is a survey for lossy image compression using Discrete Cosine Transform, it covers JPEG compression algorithm which is used for full-colour still image applications and describes all the components of it.

Color Image Quantization: A Short Review and an Application with Artificial Bee Colony Algorithm

Abstract: Color quantization is the process of reducing the number of colors in a digital image. The main objective of quantization process is that significant information should be preserved while reducing the color of an image. In other words, quantization process shouldn't cause significant information loss in the image. In this paper, a short review of color quantization is presented and a new color quantization method based on artificial bee colony algorithm (ABC) is proposed. The performance of the proposed method is evaluated by comparing it with the performance of the most widely used quantization methods such as K-means, Fuzzy C Means (FCM), minimum variance and particle swarm optimization (PSO). The obtained results indicate that the proposed method is superior to the others.

First Quantization Matrix Estimation From Double Compressed JPEG Images

Abstract: One of the most common problems in the image forensics field is the reconstruction of the history of an image or a video. The data related to the characteristics of the camera that carried out the shooting, together with the reconstruction of the (possible) further processing, allow us to have some useful hints about the originality of the visual document under analysis. For example, if an image has been subjected to more than one JPEG compression, we can state that the considered image is not the exact bitstream generated by the camera at the time of shooting. It is then useful to estimate the quantization steps of the first compression, which, in case of JPEG images edited and then saved again in the same format, are no more available in the embedded metadata. In this paper, we present a novel algorithm to achieve this goal in case of double JPEG compressed images. The proposed approach copes with the case when the second quantization step is lower than the first one, exploiting the effects of successive quantizations followed by dequantizations. To improve the results of the estimation, a proper filtering strategy together with a function devoted to find the first quantization step, have been designed. Experimental results and comparisons with the state-of-the-art methods, confirm the effectiveness of the proposed approach.

JPEG Anti-Forensics With Improved Tradeoff Between Forensic Undetectability and Image Quality

Abstract: This paper proposes a JPEG anti-forensic method, which aims at removing from a given image the footprints left by JPEG compression, in both the spatial domain and DCT domain. With reasonable loss of image quality, the proposed method can defeat existing forensic detectors that attempt to identify traces of the image JPEG compression history or JPEG anti-forensic processing. In our framework, first because of a total variation-based deblocking operation, the partly recovered DCT information is thereafter used to build an adaptive local dithering signal model, which is able to bring the DCT histogram of the processed image close to that of the original one. Then, a perceptual DCT histogram smoothing is carried out by solving a simplified assignment problem, where the cost function is established as the total perceptual quality loss due to the DCT coefficient modification. The second-round deblocking and de-calibration operations successfully bring the image statistics that are used by the JPEG forensic detectors to the normal status. Experimental results show that the proposed method outperforms the state-of-the-art methods in a better tradeoff between the JPEG forensic undetectability and the visual quality of processed images. Moreover, the application of the proposed anti-forensic method in disguising double JPEG compression artifacts is proven to be feasible by experiments.

JPEG 2000-based compression of fringe patterns for digital holographic microscopy

Abstract: With the advent of modern computing and imaging technologies, digital holography is becoming widespread in various scientific disciplines such as microscopy, interferometry, surface shape measurements, vibration analysis, data encoding, and certification Therefore, designing an efficient data representation technology is of particular importance Off-axis holograms have very different signal properties with respect to regular imagery, because they represent a recorded interference pattern with its energy biased toward the high-frequency bands This causes traditional images’ coders, which assume an underlying 1/f2 power spectral density distribution, to perform suboptimally for this type of imagery We propose a JPEG 2000-based codec framework that provides a generic architecture suitable for the compression of many types of off-axis holograms This framework has a JPEG 2000 codec at its core, extended with (1) fully arbitrary wavelet decomposition styles and (2) directional wavelet transforms Using this codec, we report significant improvements in coding performance for off-axis holography relative to the conventional JPEG 2000 standard, with Bjontegaard delta-peak signal-to-noise ratio improvements ranging from 13 to 116 dB for lossy compression in the 0125 to 200 bpp range and bit-rate reductions of up to 16 bpp for lossless compression

Compact Representation for Image Classification: To Choose or to Compress?

Abstract: In large scale image classification, features such as Fisher vector or VLAD have achieved state-of-the-art results. However, the combination of large number of examples and high dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper argues that feature selection is a better choice than feature compression. We show that strong multicollinearity among feature dimensions may not exist, which undermines feature compression's effectiveness and renders feature selection a natural choice. We also show that many dimensions are noise and throwing them away is helpful for classification. We propose a supervised mutual information (MI) based importance sorting algorithm to choose features. Combining with 1-bit quantization, MI feature selection has achieved both higher accuracy and less computational cost than feature compression methods such as product quantization and BPBC.

Digital image watermarking based on DWT-DCT: Evaluate for a new embedding algorithm

Abstract: The authenticity of content or matter is crucial factors for solving the problem of copying, modifying, and distributing the intellectual properties in an illegal way. Watermarking can resolve the stealing problem of intellectual properties. This paper considers a robust image watermarking technique based on discrete wavelet transform (WDT) and discrete cosine transform (DCT) called hybrid watermarking. The hybrid watermarking is performed by two level, three level, and four level DWT followed by respective DCT on the host image. A new embedding algorithm (NEA) of digital watermarking is proposed in this paper. The simulation results are compared with Cox's additive embedding algorithm and the NEA for additive white Gaussian noise (AWGN) attack and without attack. Both algorithms use the hybrid watermarking. The NEA gives 3.04dB and 9.33dB better pick signal to noise ratio (PSNR) compared to Cox's additive algorithm for the 4 level DWT for AWGN attack and without attack, respectively. Moreover, the NEA extracts the marked image 46 times better of Cox's additive algorithm in 2 level DWT with AWGN attack. That means, the NEA can embed larger marks and high quality marks extract from the embedded watermarking even attacking condition. Though the NEA is evaluated in this paper by comparing performances with Cox's algorithm, the performances of NEA will compare among other algorithms like Gaussian sequence, image fusion, nonlinear quantization embedding with various attacking conditions in near future.

A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images

Abstract: Predictive coding is attractive for compression on board of spacecraft due to its low computational complexity, modest memory requirements, and the ability to accurately control quality on a pixel-by-pixel basis. Traditionally, predictive compression focused on the lossless and near-lossless modes of operation, where the maximum error can be bounded but the rate of the compressed image is variable. Rate control is considered a challenging problem for predictive encoders due to the dependencies between quantization and prediction in the feedback loop and the lack of a signal representation that packs the signal’s energy into few coefficients. In this paper, we show that it is possible to design a rate control scheme intended for onboard implementation. In particular, we propose a general framework to select quantizers in each spatial and spectral region of an image to achieve the desired target rate while minimizing distortion. The rate control algorithm allows achieving lossy near-lossless compression and any in-between type of compression, e.g., lossy compression with a near-lossless constraint. While this framework is independent of the specific predictor used, in order to show its performance, in this paper, we tailor it to the predictor adopted by the CCSDS-123 lossless compression standard, obtaining an extension that allows performing lossless, near-lossless, and lossy compression in a single package. We show that the rate controller has excellent performance in terms of accuracy in the output rate, rate–distortion characteristics, and is extremely competitive with respect to state-of-the-art transform coding.

Transparent Composite Model for DCT Coefficients: Design and Analysis

Abstract: The distributions of discrete cosine transform (DCT) coefficients of images are revisited on a per image base. To better handle, the heavy tail phenomenon commonly seen in the DCT coefficients, a new model dubbed a transparent composite model (TCM) is proposed and justified for both modeling accuracy and an additional data reduction capability. Given a sequence of the DCT coefficients, a TCM first separates the tail from the main body of the sequence. Then, a uniform distribution is used to model the DCT coefficients in the heavy tail, whereas a different parametric distribution is used to model data in the main body. The separate boundary and other parameters of the TCM can be estimated via maximum likelihood estimation. Efficient online algorithms are proposed for parameter estimation and their convergence is also proved. Experimental results based on Kullback-Leibler divergence and χ2 test show that for real-valued continuous ac coefficients, the TCM based on truncated Laplacian offers the best tradeoff between modeling accuracy and complexity. For discrete or integer DCT coefficients, the discrete TCM based on truncated geometric distributions (GMTCM) models the ac coefficients more accurately than pure Laplacian models and generalized Gaussian models in majority cases while having simplicity and practicality similar to those of pure Laplacian models. In addition, it is demonstrated that the GMTCM also exhibits a good capability of data reduction or feature extraction-the DCT coefficients in the heavy tail identified by the GMTCM are truly outliers, and these outliers represent an outlier image revealing some unique global features of the image. Overall, the modeling performance and the data reduction feature of the GMTCM make it a desirable choice for modeling discrete or integer DCT coefficients in the real-world image or video applications, as summarized in a few of our further studies on quantization design, entropy coding design, and image understanding and management.

Multiple JPEG compression detection by means of Benford-Fourier coefficients

Abstract: The analysis of JPEG compressed images is one of the most studied problems in image forensics, because of the extensive use and the characteristic traces left by such coding operation In this paper, we propose a novel statistical framework for the identification of previous multiple aligned compressions in JPEG images and the estimation of the quality factors applied The method has been tested on different datasets and forensic scenarios, where up to three JPEG compressions are considered Moreover, both in the case of double and triple JPEG encoding with different quality factors, the compression history of each image is estimated The experiments show good performance and, in most cases, higher accuracies with respect to state-of-the-art methods

Algorithm for JPEG artifact reduction via local edge regeneration

Abstract: Transform coding using the discrete cosine transform is one of the most popular techniques for image and video compression. However, at low bit rates, the coded images suffer from severe visual distortions. An innovative approach is proposed that deals with artifacts in JPEG compressed images. Our algorithm addresses all three types of artifacts which are prevalent in JPEG images: blocking, and for edges blurring, and aliasing. We enhance the quality of the image via two stages. First, we remove blocking artifacts via boundary smoothing and guided filtering. Then, we reduce blurring and aliasing around the edges via a local edge-regeneration stage. We compared the proposed algorithm with other modern JPEG artifact-removal algorithms. The results demonstrate that the proposed approach is competitive, and can in many cases outperform, competing algorithms.

JPEG quantization tables selection by the firefly algorithm

Abstract: JPEG is the prevailing compression algorithm used for digital images. Compression ratio and quality depend on quantization tables that are matrixes of 64 integers. The quality of compression for many applications has to be determined not by human judgment, but by software systems that perform some processing on compressed images, based on successfulness of such processing. Since there are many such applications, there is not unique best quantization table but it has to be selected for each application. Quantization table selection is intractable combinatorial problem that can be successfully solved by swarm intelligence metaheuristics. In this paper we present framework for application of the recently introduced firefly algorithm to the quantization table selection problem for different image similarity metrics.

Post-Processing for Blocking Artifact Reduction Based on Inter-Block Correlation

Abstract: Block-based coding introduces an undesirable discontinuity between neighboring blocks in reconstructed images. This image degradation, referred to as blocking artifacts, arises mainly due to the loss of inter-block correlation in the quantization process of discrete cosine transform coefficients. In many multimedia broadcasting applications, such as a television, decoded video sequences suffer from blocking artifacts. In this paper, we present a novel post-processing algorithm based on increment of inter-block correlation aimed at reducing blocking artifacts. We first smooth the three lowest frequency discrete cosine transform (DCT) coefficients between neighboring blocks, in order to reduce blocking artifacts in the flat region, which are most sensitive to the human visual system. We then group each edge block and its matched blocks together and apply group-based filtering to increase the correlation between grouped blocks. This suppresses blocking artifacts in the edge region while preserving details. In addition, the algorithm is extended to reduce flickering artifacts as well as blocking artifacts in video sequences. Experimental results show that the proposed method successfully alleviates blocking artifacts in both images and videos coded with low bit-rates.

Reversible Data Hiding Scheme for BTC-compressed Images Based on Histogram Shifting

Abstract: In this paper, we proposed a novel reversible data hiding scheme for the compressed images for block truncation coding (BTC). In this scheme, the secret data is embedded into the compressed codes of BTC. The histogram shifting technique is employed in the proposed scheme to embed the secret data into the quantization levels of the compressed codes. After the data embedding procedure is executed, the embedded compressed codes still follow the standard format of BTC. The experimental results reveal that the proposed scheme provides good image quality of the embedded image.

Image authentication algorithm with recovery capabilities based on neural networks in the DCT domain

Abstract: In this study, the authors propose an image authentication algorithm in the DCT domain based on neural networks. The watermark is constructed from the image to be watermarked. It consists of the average value of each 8 × 8 block of the image. Each average value of a block is inserted in another supporting block sufficiently distant from the protected block to prevent simultaneous deterioration of the image and the recovery data during local image tampering. Embedding is performed in the middle frequency coefficients of the DCT transform. In addition, a neural network is trained and used later to recover tampered regions of the image. Experimental results shows that the proposed method is robust to JPEG compression and can also not only localise alterations but also recover them.

Image bit-depth enhancement via maximum-a-posteriori estimation of graph AC component

Abstract: While modern displays offer high dynamic range (HDR) with large bit-depth for each rendered pixel, the bulk of legacy image and video contents were captured using cameras with shallower bit-depth. In this paper, we study the bit-depth enhancement problem for images, so that a high bit-depth (HBD) image can be reconstructed from an input low bit-depth (LBD) image. The key idea is to apply appropriate smoothing given the constraints that reconstructed signal must lie within the per-pixel quantization bins. Specifically, we first define smoothness via a signal-dependent graph Laplacian, so that natural image gradients can nonetheless be interpreted as low frequencies. Given defined smoothness prior and observed LBD image, we then demonstrate that computing the most probable signal via maximum a posteriori (MAP) estimation can lead to large expected distortion. However, we argue that MAP can still be used to efficiently estimate the AC component of the desired HBD signal, which along with a distortion-minimizing DC component, can result in a good approximate solution that minimizes the expected distortion. Experimental results show that our proposed method outperforms existing bit-depth enhancement methods in terms of reconstruction error.

Exploiting quality and texture features to estimate age and gender from fingerprints

Abstract: Age and gender of an individual, when available, can contribute to identification decisions provided by primary biometrics and help improve matching performance. In this paper, we propose a system which automatically infers age and gender from the fingerprint image. Current approaches for predicting age and gender generally exploit features such as ridge count, and white lines count that are manually extracted. Existing automated approaches have significant limitations in accuracy especially when dealing with data pertaining to elderly females. The model proposed in this paper exploits image quality features synthesized from 40 different frequency bands, and image texture properties captured using the Local Binary Pattern (LBP) and the Local Phase Quantization (LPQ) operators. We evaluate the performance of the proposed approach using fingerprint images collected from 500 users with an optical sensor. The approach achieves prediction accuracy of 89.1% for age and 88.7% for gender.

A Novel Image Compression Algorithm for High Resolution 3D Reconstruction

Abstract: This research presents a novel algorithm to compress high-resolution images for accurate structured light 3D reconstruction. Structured light images contain a pattern of light and shadows projected on the surface of the object, which are captured by the sensor at very high resolutions. Our algorithm is concerned with compressing such images to a high degree with minimum loss without adversely affecting 3D reconstruction. The Compression Algorithm starts with a single level discrete wavelet transform (DWT) for decomposing an image into four sub-bands. The sub-band LL is transformed by DCT yielding a DC-matrix and an AC-matrix. The Minimize-Matrix-Size Algorithm is used to compress the AC-matrix while a DWT is applied again to the DC-matrix resulting in LL2, HL2, LH2 and HH2 sub-bands. The LL2 sub-band is transformed by DCT, while the Minimize-Matrix-Size Algorithm is applied to the other sub-bands. The proposed algorithm has been tested with images of different sizes within a 3D reconstruction scenario. The algorithm is demonstrated to be more effective than JPEG2000 and JPEG concerning higher compression rates with equivalent perceived quality and the ability to more accurately reconstruct the 3D models.

System and method for depth based adaptive streaming of video information

Abstract: Systems and methods for adaptive bitrate streaming of video information are provided. If a depth map can be derived or is independently available for the image sequence, the depth map can be used to selectively blur (effectively reducing the resolution of) background areas and to select encoding quantization parameters by image region in order to throttle the bitrate. In a cloud-based gaming application, the depth information can be used to selectively render background layers at lower resolutions thereby improving the compression efficiency of the rendered images.

A Benford-Fourier JPEG compression detector

Abstract: Intrinsic statistical properties of natural uncompressed images can be used in image forensics for detecting traces of previous processing operations. In this paper, we extend the recent theoretical analysis of Benford-Fourier coefficients and propose a novel forensic detector of JPEG compression traces in images stored in an uncompressed format. The classification is based on a binary hypothesis test for which we can derive theoretically the confidence intervals, thus avoiding any training phase. Experiments on real images and comparisons with state-of-art techniques show that the proposed detector outperforms existing ones and overcomes issues due to dataset-dependency.

Double watermarks of 3D mesh model based on feature segmentation and redundancy information

Abstract: This paper presents a new robust, blind and good imperceptibility 3D mesh double watermarks algorithm. Two different kinds of watermarks are embedded into one 3D mesh model. One watermarking algorithm based on mesh feature segmentation and the DCT transformation, the other based on redundancy information of 3D model. The two watermarks do not disturb each other during embedding and extracting. Several mesh models are applied to test the robustness, imperceptibility and efficiency of the proposed algorithm. The experimental results show that the proposed watermark scheme can not only keep good imperceptibility but also resist various attacks, such as similarity transformations (translation, rotation, scaling and combinations of the three operations), file attack, signal processing attacks (noising, smoothing and vertex coordinate quantization) and connectivity attacks (cropping).

Performance evaluation of the emerging JPEG XT image compression standard

Abstract: The upcoming JPEG XT is under development for High Dynamic Range (HDR) image compression. This standard encodes a Low Dynamic Range (LDR) version of the HDR image generated by a Tone-Mapping Operator (TMO) using the conventional JPEG coding as a base layer and encodes the extra HDR information in a residual layer. This paper studies the performance of the three profiles of JPEG XT (referred to as profiles A, B and C) using a test set of six HDR images. Four TMO techniques were used for the base layer image generation to assess the influence of the TMOs on the performance of JPEG XT profiles. Then, the HDR images were coded with different quality levels for the base layer and for the residual layer. The performance of each profile was evaluated using Signal to Noise Ratio (SNR), Feature SIMilarity Index (FSIM), Root Mean Square Error (RMSE), and CIEDE2000 color difference objective metrics. The evaluation results demonstrate that profiles A and B lead to similar saturation of quality at the higher bit rates, while profile C exhibits no saturation. Profiles B and C appear to be more dependent on TMOs used for the base layer compared to profile A.