Enhancing LTW image encoder with perceptual coding and GPU-optimized 2D-DWT transform
23 Aug 2013-EURASIP Journal on Advances in Signal Processing (Springer International Publishing)-Vol. 2013, Iss: 1, pp 141
TL;DR: This work proposes 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.
Abstract: 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.
Citations
More filters
TL;DR: This contribution focuses on different topics covered by the special issue titled ‘Hardware Implementation of Machine vision Systems’ including FPGAs, GPUS, embedded systems, multicore implementations for image analysis such as edge detection, segmentation, pattern recognition and object recognition/interpretation.
Abstract: This contribution focuses on different topics covered by the special issue titled ‘Hardware Implementation of Machine vision Systems’ including FPGAs, GPUS, embedded systems, multicore implementations for image analysis such as edge detection, segmentation, pattern recognition and object recognition/interpretation, image enhancement/restoration, image/video compression, image similarity and retrieval, satellite image processing, medical image processing, motion estimation, neuromorphic and bioinspired vision systems, video processing, image formation and physics based vision, 3D processing/coding, scene understanding, and multimedia.
7 citations
Cites methods from "Enhancing LTW image encoder with pe..."
...In the article entitled ‘Enhancing LTW image encoder with perceptual coding and GPU-optimized 2D-DWT transform’ [10] by Miguel Martínez-Rach et al....
[...]
01 Jan 2008
6 citations
References
More filters
TL;DR: A novel framework for IQA to mimic the human visual system (HVS) by incorporating the merits from multiscale geometric analysis (MGA), contrast sensitivity function (CSF), and the Weber's law of just noticeable difference (JND) is developed.
Abstract: Reduced-reference (RR) image quality assessment (IQA) has been recognized as an effective and efficient way to predict the visual quality of distorted images. The current standard is the wavelet-domain natural image statistics model (WNISM), which applies the Kullback-Leibler divergence between the marginal distributions of wavelet coefficients of the reference and distorted images to measure the image distortion. However, WNISM fails to consider the statistical correlations of wavelet coefficients in different subbands and the visual response characteristics of the mammalian cortical simple cells. In addition, wavelet transforms are optimal greedy approximations to extract singularity structures, so they fail to explicitly extract the image geometric information, e.g., lines and curves. Finally, wavelet coefficients are dense for smooth image edge contours. In this paper, to target the aforementioned problems in IQA, we develop a novel framework for IQA to mimic the human visual system (HVS) by incorporating the merits from multiscale geometric analysis (MGA), contrast sensitivity function (CSF), and the Weber's law of just noticeable difference (JND). In the proposed framework, MGA is utilized to decompose images and then extract features to mimic the multichannel structure of HVS. Additionally, MGA offers a series of transforms including wavelet, curvelet, bandelet, contourlet, wavelet-based contourlet transform (WBCT), and hybrid wavelets and directional filter banks (HWD), and different transforms capture different types of image geometric information. CSF is applied to weight coefficients obtained by MGA to simulate the appearance of images to observers by taking into account many of the nonlinearities inherent in HVS. JND is finally introduced to produce a noticeable variation in sensory experience. Thorough empirical studies are carried out upon the LIVE database against subjective mean opinion score (MOS) and demonstrate that 1) the proposed framework has good consistency with subjective perception values and the objective assessment results can well reflect the visual quality of images, 2) different transforms in MGA under the new framework perform better than the standard WNISM and some of them even perform better than the standard full-reference IQA model, i.e., the mean structural similarity index, and 3) HWD performs best among all transforms in MGA under the framework.
251 citations
"Enhancing LTW image encoder with pe..." refers background in this paper
...So it is better not to trust on how PNSR ranks quality and use instead a perceptually inspired quality assessment metric like VIF that, as stated in [17,19], has a better correlation with the human perception of image quality....
[...]
...One of the best behaving objective quality assessment metrics is visual information fidelity (VIF) [7], which has been proven [17,19] to have a better correlation with subjective perception than other metrics that are commonly used for encoder comparisons [14,20]....
[...]
...If further decompositions of the frequency domain are done, for example, a finer association could be done between frequency and weights using packet wavelets [17]....
[...]
..., subjective test results) [14,17,19,20]....
[...]
TL;DR: Two new techniques that implement the contrast sensitivity function at significantly higher precision are presented, adapting even to local variations of the spatial frequencies within a decomposition subband, and are compared to conventional CSF-schemes.
Abstract: The visual efficiency of an image compression technique depends directly on the amount of visually significant information it retains. By "visually significant" we mean information to which a human observer is most sensitive. The overall sensitivity depends on aspects such as contrast, color, spatial frequency, and so forth. One important aspect is the inverse relationship between contrast sensitivity and spatial frequency. This is described by the contrast sensitivity function (CSF). In compression algorithms the CSF can be exploited to regulate the quantization step-size to minimize the visibility of compression artifacts. Existing CSF implementations for wavelet-based image compression use the same quantization step-size for a large range of spatial frequencies. This is a coarse approximation of the CSF. This paper presents two new techniques that implement the CSF at significantly higher precision, adapting even to local variations of the spatial frequencies within a decomposition subband. The approaches can be used for luminance as well as color images. For color perception three different CSFs describe the sensitivity. The implementation technique is the same for each color band. Implemented into the JPEG2000 compression standard, the new techniques are compared to conventional CSF-schemes. The proposed techniques turn out to be visually more efficient than previously published methods. However, the emphasis of this paper is on how the CSF can be implemented in a precise and locally adaptive way, and not on the superior performance of these techniques.
174 citations
"Enhancing LTW image encoder with pe..." refers background or methods in this paper
...3 Using the CSF In [9], the authors explained how the CSF can be implemented in wavelet-based codecs....
[...]
...2 The contrast sensitivity function In [9], the authors explained how the sensitivity to contrast of the HVS can be exploited by means of the CSF curve to enhance the perceptual or subjective quality of the DWT-encoded images....
[...]
...As mentioned, the 2D-DWT computation stage runs on a GPU and includes the perceptual weighting based on the CSF and implemented as an invariant scaling factor weighting (ISFW) [9] that weights the obtained coefficients depending on the importance that the frequency subband has for theHVS contrast sensitivity....
[...]
...Although an observer can look at the images from any distance, as stated in [9], the assumption of ‘worst case...
[...]
...However, we introduce the CSF in the encoder using the ISFW strategy proposed also in [9]....
[...]
07 May 1996
TL;DR: A fast technique for identifying zerotrees for all dominant passes in the encoder prior to encoding is presented, which can be performed in parallel with the wavelet transform operation and thus is efficient for both hardware and software implementations.
Abstract: The embedded zerotree wavelet (EZW) algorithm has become a popular benchmark for image compression algorithms. The current paper presents a fast technique for identifying zerotrees for all dominant passes in the encoder prior to encoding. The key is initializing a data structure called a zerotree map with the largest power of two smaller than a given coefficient, and then completing the zerotree map by bitwise-ORing the values of potential parents with those of their children. This simple operation can be performed in parallel with the wavelet transform operation and thus is efficient for both hardware and software implementations.
53 citations
"Enhancing LTW image encoder with pe..." refers methods in this paper
...wavelet coefficient-trees and successive approximations was introduced by the EZW [13] with a bit-plane coding approximation....
[...]
TL;DR: A new image compression algorithm is proposed based on the efficient construction of wavelet coefficient lower trees, which presents state-of-the-art compression performance, whereas its complexity is lower than the one presented in other wavelet coders, like SPIHT and JPEG 2000.
Abstract: In this paper, a new image compression algorithm is proposed based on the efficient construction of wavelet coefficient lower trees. The main contribution of the proposed lower-tree wavelet (LTW) encoder is the utilization of coefficient trees, not only as an efficient method of grouping coefficients, but also as a fast way of coding them. Thus, it presents state-of-the-art compression performance, whereas its complexity is lower than the one presented in other wavelet coders, like SPIHT and JPEG 2000. Fast execution is achieved by means of a simple two-pass coding and one-pass decoding algorithm. Moreover, its computation does not require additional lists or complex data structures, so there is no memory overhead. A formal description of the algorithm is provided, while reference software is also given. Numerical results show that our codec works faster than SPIHT and JPEG 2000 (up to three times faster than SPIHT and fifteen times faster than JPEG 2000), with similar coding efficiency
48 citations
"Enhancing LTW image encoder with pe..." refers background in this paper
...More details about the coding and decoding algorithms, along with a formal description and an example of use, can be found in [5,12]....
[...]
28 Mar 2000
TL;DR: In this paper, sign coding is examined in detail in the context of an embedded wavelet image coder and it is shown that PSNR improvements of up to 0.7 dB are possible from an efficient modeling and entropy coding of the coefficient signs, combined with a new extrapolation technique, which is used to improve the final estimate of insignificant coefficients.
Abstract: Wavelet transform coefficients are defined by both a magnitude and a sign. While promising algorithms exist for efficiently coding the transform coefficient magnitudes, current wavelet image coding algorithms are not efficient at coding the sign of the transform coefficients. It is generally assumed that there is no compression gain to be obtained from entropy coding of the sign. Only recently have some authors begun to investigate this component of wavelet image coding. In this paper, sign coding is examined in detail in the context of an embedded wavelet image coder. It is shown that PSNR improvements of up to 0.7 dB are possible from an efficient modeling and entropy coding of the coefficient signs, combined with a new extrapolation technique, which is used to improve the final estimate of insignificant coefficients. These sign coding techniques are applicable to any genre (e.g., zero-tree, context-model) of the embedded wavelet image coder.
31 citations
"Enhancing LTW image encoder with pe..." refers background in this paper
...The encoder exploits the sign neighborhood correlation of wavelet subband type (HL,LH,HH) as Deever assesses in [2] by encoding the prediction of the sign (success of failure)....
[...]