PSNR Measures Research Articles

Visual-PSNR measure of image quality

Objective assessment of image quality is important in numerous image and video processing applications. Many objective measures of image quality have been developed for this purpose, of which peak signal-to-noise ratio PSNR is one of the simplest and commonly used. However, it sometimes does not match well with objective mean opinion scores (MOS). This paper presents a novel objective full-reference measure of image quality (VPSNR), which is a modified PSNR measure. It will be shown that VPSNR takes into account some features of the human visual system (HVS). The performance of VPSNR is validated using a data set of four image databases, and in this article it is shown that for images compressed by block-based compression algorithms (like JPEG) the proposed measure in the pixel domain matches well with MOS.

De-Interlacing Algorithm Using Weighted Least Squares

This paper presents a weighted least squares-based intrafield de-interlacing algorithm. First, we formulate the estimation of the missing pixels as a maximum a posteriori (MAP) framework. We deduce the weighted least squares structure from MAP based on the analysis of the statistic model of the original high-resolution images and the associated statistical model of the given low-resolution images and original high-resolution images. The weights affect the estimation of the statistical model. We also design adaptive weights to match regions with different properties. The method is compared with other de-interlacing algorithms in terms of PSNR and SSIM objective quality measures and de-interlacing speed. It was found to provide excellent performance and the best quality-speed tradeoff among the methods studied.

Calculation of average coding efficiency based on subjective quality scores

The Bjøntegaard model is widely used to calculate the coding efficiency between different codecs. However, this model might not be an accurate predictor of the true coding efficiency as it relies on PSNR measurements. Therefore, in this paper, we propose a model to calculate the average coding efficiency based on subjective quality scores, i.e., mean opinion scores (MOS). We call this approach Subjective Comparison of ENcoders based on fItted Curves (SCENIC). To consider the intrinsic nature of bounded rating scales, a logistic function is used to fit the rate–distortion (R–D) values. The average MOS and bit rate differences are computed between the fitted R–D curves. The statistical property of subjective scores is considered to estimate corresponding confidence intervals on the calculated average MOS and bit rate differences. The proposed model is expected to report more realistic coding efficiency as PSNR is not always correlated with perceived visual quality.

Single Image Super-Resolution via Non Sub-sample Contourlet Transform based Learning and a Gabor Prior

Enhancing the quality of image is a continuous process in image processing related research activities. For some applications it becomes essential to have best quality of image such as in forensic department, where in order to retrieve maximum possible information, image has to be enlarged in terms of size, with higher resolution and other features associated with it. Such obtained high quality images have also a concern in satellite imaging, medical science, High Definition Television (HDTV), etc. In this paper a novel approach of getting high resolution image from a single low resolution image is discussed. The Non Sub-sampled Contourlet Transform (NSCT) based learning is used to learn the NSCT coefficients at the finer scale of the unknown high-resolution image from a dataset of high resolution images. The cost function consisting of a data fitting term and a Gabor prior term is optimized using an Iterative Back Projection (IBP). By making use of directional decomposition property of the NSCT and the Gabor filter bank with various orientations, the proposed method is capable to reconstruct an image with less edge artifacts. The validity of the proposed approach is proven through simulation on several images. RMS measures, PSNR measures and illustrations show the success of the proposed method.

Motion based video super resolution using edge directed interpolation and complex wavelet transform

This paper proposes a novel super resolution technique using dual tree complex wavelet transform (DT-CWT) and new edge directional interpolation (NEDI) based on localizing motion blocks in consecutive frames. A super resolution process is applied on the extracted motion blocks and the static region of the frame to generate the low frequency subbands of DT-CWT. DT-CWT decomposition followed by NEDI generates the high frequency subbands in order to obtain the components required for inverse DT-CWT (IDT-CWT). Finally, the super resolved output frame is generated by composing the obtained high and low frequency subbands using IDT-CWT. Experimental results based on PSNR measures illustrate the advantage of proposed technique over the state of the art video resolution enhancement methods.

A proficient wavelet-based video encoder through the motion estimation algorithm

In several multimedia applications, image and video coding plays a significant role. In many applications, sceneries such as broadcast services over satellite and terrestrial channels, digital video storage, wires and wireless conversational services and digital video communication are used. It is not possible to store full digital video without processing. Video compression reduces the data used to represent digital video images and is a combination of spatial image compression and temporal motion compensation. The main aim of our research is to develop an efficient video compression system. The proposed system consists of three steps. At first, wavelet decomposition is applied to the I–frame and the resulting coefficients are quantised using the listless SPECK (LSK) algorithm. In the next step, motion estimation is done using adaptive rood search with the spatio–temporal correlation method (ARS–ST) and it calculates the distance between the P–frame and I–frame blocks. In the final step, the difference between the original and the predicted P–frame is evaluated and is known as residual. To produce good quality predictive frames, this residual is transmitted along with motion vectors (MVs) and to decrease its size, it is coded using LSK. The proposed video compression technique uses different videos for assessment and by determining the PSNR values, compression efficiency is estimated. By comparing the proposed system with existing systems, it is seen that our system effectively compresses videos with remarkable PSNR measurements.

De-speckling 2D-Discrete Wavelet Transform with Hard Threshold Stage

In this work, a new method is implemented for removing noise from gray scale image that depends on two-dimensional discrete wavelet transform and Threshold stage (hard threshold). This paper represents the algorithm to remove the speckle noise by using logarithm operation. This operation changes the multiplicative noise to additive noise. So that, the removing operation becomes easier. The Matlab program is used to build the Algorithm and measure the PSNR and other measurement criteria as (NMV, NV, NSD, ENL and PSNR) to study the effect of removing noise from corrupted image. The PSNR reaches to 24dB which is very satisfactory result in the reconstructed image, while the maximum value of ENL is 2.23 * 106, and the minimum value of NMV, NV, NSD which is equal to 6.79, 2.67*10-5, 46* 10-4 respectively gives a smoother and cleaner image. The universal Threshold is applied in high frequency coefficient (i.e. the LH, HL, and HH-sub band of image) to remove the speckle noise and the low frequency coefficient (LL-sub band of image) is still without any change.

A Natural Image Compression Approach Based on Independent Component Analysis and Visual Saliency Detection

In this paper, a natural image compression method is proposed based on independent component analysis (ICA) and visual saliency detection. The proposed compression method learns basis functions trained from data using ICA to transform the image at first; and then sets percentage of the zero coefficient number in the total transforming coefficients. After that, transforming coefficients are sparser which indicates further improving of compression ratio. Next, the compression method performance is compared with the discrete cosine transform (DCT). Evaluation through both the usual PSNR and Structural Similarity Index (SSIM) measurements showed that proposed compression method is more robust than DCT. And finally, we proposed a visual saliency detection method to detect automatically the important region of image which is not or lowly compressed while the other regions are highly compressed. Experiment shows that the method can guarantee the quality of important region effectively.

Perceptual-based distributed video coding

In this paper, we propose a perceptual-based distributed video coding (DVC) technique. Unlike traditional video codecs, DVC applies video prediction process at the decoder side using previously received frames. The predicted video frames (i.e., side information) contain prediction errors. The encoder then transmits error-correcting parity bits to the decoder to reconstruct the video frames from side information. However, channel codes based on i.i.d. noise models are not always efficient in correcting video prediction errors. In addition, some of the prediction errors do not cause perceptible visual distortions. From perceptual coding point of view, there is no need to correct such errors. This paper proposes a scheme for the decoder to perform perceptual quality analysis on the predicted side information. The decoder only requests parity bits to correct visually sensitive errors. More importantly, with the proposed technique, key frames can be encoded at higher rates while still maintaining consistent visual quality across the video sequence. As a result, even the objective PSNR measure of the decoded video sequence will increase too. Experimental results show that the proposed technique improves the R-D performance of a transform domain DVC codec both subjectively and objectively. Comparisons with a well-known DVC codec show that the proposed perceptual-based DVC coding scheme is very promising for distributed video coding framework.

A Low Complexity Switched Method for Enhancement of Spatial Resolution of Images

This paper presents a novel approach of image interpolation based on the switching of new edge directed interpolation (NEDI) and single pass interpolation algorithm ( SPIA ) and switching is based upon the % of edges present in the blocks of the image. The switching of this interpolation algorithm is block based instead of image based or pixel based. Imperially we found that NEDI methods is better applicable for smoother images (variation among the pixels is less) while SPIA method works better on detailed images (more variation among the pixels), because of the type of pixels used in the process interpolation. So, a hybrid scheme of combining NEDI method and SPIA method is used for better prediction of HR image. The proposed algorithm produces the better results for different varieties of images in terms of both PSNR measurement and subjective visual quality with low computational complexity as compare to recently developed interpolation algorithms.

Image Diffusion In Connection With Robust M-Estimators

In this paper, we introduce a nonlinear diffusion method for image denoising using robust M-estimators. In the proposed diffusion model, the diffusivity function is replaced by robust M-estimators weight function and the modulus of gradient in a diffusivity function is substituted by the average of local gradient in a 3x3 window. The proposed method is tested on a number of benchmarks and compared to Weickert‘s diffusivity function. The method reports on high visual quality and high PSNR measures compared to Weickert‘s diffusivity.

A Natural Image Compression Approach Based on ICA and Visual Saliency Detection

In this paper, a natural image compression method is proposed based on independent component analysis (ICA) and visual saliency detection. The proposed compression method learns basis functions trained from data using ICA to transform the image at first; and then sets percentage of the zero coefficient number in the total transforming coefficients. After that, transforming coefficients are sparser which indicates further improving of compression ratio. Next, the compression method performance is compared with the discrete cosine transform (DCT). Evaluation through both the usual PSNR and Structural Similarity Index (SSIM) measurements showed that proposed compression method is more robust to DCT. And finally, we proposed a visual saliency detection method to detect automatically the important region of image which is not or low compressed while the other regions are highly compressed. Experiment shows that the method can guarantee the quality of important region effectively.

A new fuzzy-based decision algorithm for high-density impulse noise removal

This paper proposes a new efficient fuzzy-based decision algorithm (FBDA) for the restoration of images that are corrupted with high density of impulse noises. FBDA is a fuzzy-based switching median filter in which the filtering is applied only to corrupted pixels in the image while the uncorrupted pixels are left unchanged. The proposed algorithm computes the difference measure for each pixel based on the central pixel (corrupted pixel) in a selected window and then calculates the membership value for each pixel based on the highest difference. The algorithm then eliminates those pixels from the window with very high and very low membership values, which might represent the impulse noises. Median filter is then applied to the remaining pixels in the window to get the restored value for the current pixel position. The proposed algorithm produces excellent results compared to conventional method such as standard median filter (SMF) as well as some advanced techniques such as adaptive median filters (AMF), efficient decision-based algorithm (EDBA), improved efficient decision-based algorithm (IDBA) and boundary discriminative noise detection (BDND) switching median filter. The efficiency of the proposed algorithm is evaluated using different standard images. From experimental analysis, it has been found that FBDA produces better results in terms of both quantitative measures such as PSNR, SSIM, IEF and qualitative measures such as Image Quality Index (IQI).

Visual attention guided bit allocation in video compression

A visual attention-based bit allocation strategy for video compression is proposed. Saliency-based attention prediction is used to detect interesting regions in video. From the top salient locations from the computed saliency map, a guidance map is generated to guide the bit allocation strategy through a new constrained global optimization approach, which can be solved in a closed form and independently of video frame content. Fifty video sequences (300 frames each) and eye-tracking data from 14 subjects were collected to evaluate both the accuracy of the attention prediction model and the subjective quality of the encoded video. Results show that the area under the curve of the guidance map is 0.773 ± 0.002, significantly above chance (0.500). Using a new eye-tracking-weighted PSNR (EWPSNR) measure of subjective quality, more than 90% of the encoded video clips with the proposed method achieve better subjective quality compared to standard encoding with matched bit rate. The improvement in EWPSNR is up to over 2 dB and on average 0.79 dB.

High‐Performance Region‐of‐Interest Image Error Concealment with Hiding Technique

Recently region‐of‐interest (ROI) based image coding is a popular topic. Since ROI area contains much more important information for an image, it must be prevented from error decoding while suffering from channel lost or unexpected attack. This paper presents an efficient error concealment method to recover ROI information with a hiding technique. Based on the progressive transformation, the low‐frequency components of ROI are encoded to disperse its information into the high‐frequency bank of original image. The capability of protection is carried out with extracting the ROI coefficients from the damaged image without increasing extra information. Simulation results show that the proposed method can efficiently reconstruct the ROI image when ROI bit‐stream occurs errors, and the measurement of PSNR result outperforms the conventional error concealment techniques by 2 to 5 dB.

SNR and PSNR measurements and analysis of median filtering for the removal of impulse noise from CR imaging

In this paper, the authors showed that the removal of impulse noise in CR images was implemented using variety of median filters and SNR/PSNR measurements. They used three kinds of medical images-hand, skull, and knee- for experimental results. But the noise in CR image was only the impulse noise. In real medical image, the noise of an image would be very different type. Therefore. the lack of experimental results using different noise in CR images is one flaw.

Low Bit-Rate Image Compression via Adaptive Down-Sampling and Constrained Least Squares Upconversion

Recently, many researchers started to challenge a long-standing practice of digital photography: oversampling followed by compression and pursuing more intelligent sparse sampling techniques. In this paper, we propose a practical approach of uniform down sampling in image space and yet making the sampling adaptive by spatially varying, directional low-pass prefiltering. The resulting down-sampled prefiltered image remains a conventional square sample grid, and, thus, it can be compressed and transmitted without any change to current image coding standards and systems. The decoder first decompresses the low-resolution image and then upconverts it to the original resolution in a constrained least squares restoration process, using a 2-D piecewise autoregressive model and the knowledge of directional low-pass prefiltering. The proposed compression approach of collaborative adaptive down-sampling and upconversion (CADU) outperforms JPEG 2000 in PSNR measure at low to medium bit rates and achieves superior visual quality, as well. The superior low bit-rate performance of the CADU approach seems to suggest that oversampling not only wastes hardware resources and energy, and it could be counterproductive to image quality given a tight bit budget.

Peak-Shaped-Based Steganographic Technique for JPEG Images

A novel model-based steganographic technique for JPEG images is proposed where the model, derived from heuristic assumptions about the shape of the DCT frequency histograms, is dependent on a stegokey. The secret message is embedded in DCT domain through an accurate selection of the potentially modifiable coefficients, taking into account their visual and statistical relevancy. A novel block measure, named discrepancy, is introduced in order to select suitable areas for embedding. The visual impact of the steganographic technique is evaluated through PSNR measures. State-of-the-art steganalytical test is also performed to offer a comparison with the original model-based techniques.

Discrete wavelet transformation of an image based on genetic-algorithm clustering

In this paper the image compression problem is analyzed using genetic clustering algorithms based on the pixels of the image. The main problem to solve is to find an algorithm that performs the clustering efficiently. The possibility of solving clustering problems with genetic algorithms provides optimal solution. The present work makes use of genetic clustering algorithms to obtain an ordered representation of the image and then applies the DWT (Discrete Wavelet Transformation) to compress the image. The image quality is measured using PSNR measure. The genetic algorithm (GA)-based compression image has more PSNR value compared to the image without GA. It shows that the quality of the image is good for GA based compression. Also it reduces the problem of memory utilization efficiently due to compression and at the same time quality of the image is maintained due to GA.

Discrete wavelet transformation of an image based on genetic-algorithm clustering

In this paper the image compression problem is analyzed using genetic clustering algorithms based on the pixels of the image. The main problem to solve is to find an algorithm that performs the clustering efficiently. The possibility of solving clustering problems with genetic algorithms provides optimal solution. The present work makes use of genetic clustering algorithms to obtain an ordered representation of the image and then applies the DWT (Discrete Wavelet Transformation) to compress the image. The image quality is measured using PSNR measure. The genetic algorithm (GA)-based compression image has more PSNR value compared to the image without GA. It shows that the quality of the image is good for GA based compression. Also it reduces the problem of memory utilization efficiently due to compression and at the same time quality of the image is maintained due to GA. Keywords: Genetic algorithms, clustering, image compression, discrete wavelet transformation.