Peak-to-peak Signal-to-noise Ratio Research Articles

A Multiple Criteria-Based Cost Function Using Wavelet and Edge Transformation for Medical Image Steganography

Abstract With the ever-increasing need for concealing messages within cover media like image, video, and audio, numerous attempts have been developed for steganography. Most of the steganographic techniques perform their embedding operation on the cover image without selecting a better location. The right selection of location for embedding the information can lead to high imperceptibility and robustness. Accordingly, in this paper, we develop a new cost function for estimating the cost of every pixel to identify the good location to embed the message data. The proposed cost estimation procedure utilizes multiple parameters like wavelet coefficient, edge transformation, and pixel intensity. The proposed cost matrix is then utilized to embed the message data into the cover media using an embedding integer. The proposed steganographic technique is experimented with two magnetic resonance brain images, and the results are analyzed with the peak-to-peak signal-to-noise ratio (PSNR) and mean square error. The robustness analysis ensured that the proposed steganographic technique outperforms the existing methods by reaching the maximum PSNR of 72.74 dB.

Multi-frame example-based super-resolution using locally directional self-similarity

This paper presents a multi-frame superresolution approach to reconstruct a high-resolution image from several low-resolution video frames. The proposed algorithm consists of three steps: i) definition of a local search region for the optimal patch using motion vectors, ii) adaptive selection of the optimum patch based on lowresolution image degradation model, and iii) combination of the optimum patch and reconstructed image. As a result, the proposed algorithm can remove interpolation artifacts using directionally adaptive patch selection based on the lowresolution image degradation model. Moreover, superresolved images without distortion between consecutive frames can be generated. The proposed method provides a significantly improved super-resolution performance over existing methods in the sense of both subjective and objective measures including peak-to-peak signal-to-noise ratio (PSNR), structural similarity measure (SSIM), and naturalness image quality evaluator (NIQE). The proposed multi-frame super-resolution algorithm is designed for realtime video processing hardware by reducing the search region for optimal patches, and suitable for consumer imaging devices including ultra-high-definition (UHD) digital televisions, surveillance systems, and medical imaging systems for image restoration and enhancement.

UHD TV image enhancement using example-based spatially adaptive image restoration filter

This paper presents a novel image restoration algorithm using examples and truncated constrained least squares (TCLS) filter for ultra-high definition (UHD) television systems. The proposed approach consists of three steps: (i) generation of the patch dictionary using multiple-step image blurring, (ii) selection of the optimum patch based on the orientation and the amount of blurring, and (iii) combination of the selected patch in the dictionary and its filtered version by the TCLS restoration filter for reducing the patch mismatch error. In the proposed algorithm, a complicated point-spread-function (PSF) estimation process is replaced with the generation of multiple, differently blurred patches. Furthermore, the patch dictionary is made by orientation-based classification to reduce the time to search the optimum patch. Experimental results show that the proposed algorithm can restore more natural images with less synthetic artifacts than existing methods. The proposed method provides a significantly improved restoration performance over existing methods in the sense of both subjective and objective measures including peak-to-peak signal-to-noise ratio (PSNR) and structural similarity measure (SSIM).

Construction of New S-Boxes Over Finite Field and Their Application to Watermarking

In this work, we develop an imperceptible watermarking technique for images that employ substitution boxes constructed over Galois field GF(24). The strength of the proposed substitution box (S-box) is analyzed and its suitability is investigated for watermarking applications by applying statistical methods, which include entropy, contrast, correlation, energy, homogeneity, mean of absolute deviation (MAD), mean square error (MSE), peak-to-peak signal to noise ratio (PSNR), and structural similarity (SSIM) paradigm analysis. The application of the proposed S-box is presented for embedding copyright information in images.

커브형 집적영상에서 부분적으로 가려진 먼 거리 물체 인식 향상을 위한 DPM 방법

본 논문에서는 커브형 집적영상 시스템에서 부분적으로 가려진 먼 거리 3차원 물체의 인식 향상을 위한 새로운 direct pixel-mapping (DPM)방법을 제안한다. 제안 방법은 커브형 집적영상 시스템에서 DPM 방법에 의해 먼 거리에 위치한 3차원 물체로부터 픽업된 요소영상배열 (elemental image array, EIA)은 가시적으로 가까운 거리에서 픽업한 것과 같은 새로운 요소영상배열을 생성한다. 이러한 특성은 재생한 3차원 물체 영상의 해상도를 향상 시킬 수 있고, 이로 인하여 먼 거리에 위치한 3차원 물체에 대한 인식 성능을 향상 시킬 수 있다. 컴퓨터적 실험결과와 기존 방법과의 비교를 통하여 제안방법으로 재생한 물체의 PSNR과 NCC의 값이 평균 1.75dB와 4.56% 향상됨을 확인할 수 있었다. In this paper, we propose a novel approach to enhance the recognition performance of a far and partially occluded three-dimensional (3-D) target in computational curving-effective integral imaging (CEII) by using the direct pixel-mapping (DPM) method. With this scheme, the elemental image array (EIA) originally picked up from a far and partially occluded 3-D target can be converted into a new EIA just like the one virtually picked up from a target located close to the lenslet array. Due to this characteristic of DPM, resolution and quality of the reconstructed target image can be highly enhanced, which results in a significant improvement of recognition performance of a far 3-D object. Experimental results reveal that image quality of the reconstructed target image and object recognition performance of the proposed system have been improved by 1.75 dB and 4.56% on the average in PSNR (peak-to-peak signal-to-noise ratio) and NCC (normalized correlation coefficient), respectively, compared to the conventional system.

Fast super-resolution algorithms using one-dimensional patch-based training and directional interpolation

This study proposes fast super-resolution algorithms to up-scale an input low-resolution image into a high-resolution image. Conventional learning-based super-resolution algorithms require large memory space to store a huge amount of synthesis information, and they require significant computation because of the large number of two-dimensional matching operations. To mitigate this problem, the authors train a dictionary using one-dimensional patch-based training and K-means clustering at the learning phase, and they use one-dimensional matching and interpolation based on the trained dictionary at the synthesis phase. Such one-dimensional content-adaptive interpolation is applied separately in horizontal and vertical directions. In addition, the authors propose a hybrid algorithm in which directional interpolation is utilised for vertical interpolation to further reduce the dictionary size and the so called staircase artefact. Simulation results show that the proposed algorithm has higher peak-to-peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) values while providing significantly smaller dictionary size and faster computation than the latest learning-based super-resolution algorithm.

Low complexity weighted two-bit transforms based multiple candidate motion estimation

A low complexity weighted two-bit transforms (2BT) based multiple candidate motion estimation algorithm is proposed in this paper. By exploiting almost the identical operations in two different matching error criteria, we can efficiently determine two best motion vectors according to the respective matching criteria with negligible complexity increase. And with additional local square search, the proposed algorithm can enhance the overall motion estimation accuracy substantially. Experimental results show that the proposed algorithm achieves peak-to-peak signal-tonoise ratio (PSNR) gains about 0.55dB on average compared with the conventional 2BT-based motion estimation <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

Performance-enhanced recognition of a far and partially occluded 3-D object by use of direct pixel-mapping in computational curving-effective integral imaging

In this paper, we propose a novel approach to enhance the recognition performance of a far and partially occluded three-dimensional (3-D) target in computational curving-effective integral imaging by using the direct pixel-mapping (DPM) method. With this scheme, the elemental image array (EIA) originally picked up from a far and partially occluded 3-D target can be converted into a new EIA just like the one virtually picked up from a target located close to the lenslet array. Due to this characteristic of DPM, resolution and quality of the reconstructed target image can be highly enhanced, which results in a significant improvement of recognition performance of a far 3-D object. In addition, the computational time required for reconstruction of a far 3-D target could be also reduced because the distance between the lenslet array and image plane is virtually shortened in the new EIA transformed by DPM. Experimental results reveal that image quality of the reconstructed target image and object recognition performance of the proposed system have been improved by 1.75 dB and 4.56% on the average in PSNR (peak-to-peak signal-to-noise ratio) and NCC (normalized correlation coefficient), respectively, compared to the conventional system.

Enhanced two-bit transform based motion estimation via extension of matching criterion

Improved two-bit transform-based motion estimation algorithms are proposed in this paper. By extending the typical two-bit transform (2BT) matching criterion, the proposed algorithms enhance the motion estimation accuracy with almost the same computational complexity, while preserving the binary matching characteristic. Experimental results show that the proposed algorithm achieves peak-to-peak signal-to-noise ratio (PSNR) gains up to 0.42 dB compared with the conventional 2BT-based motion estimation.

IPTV 서비스에서 패킷손실에 의한 비디오품질 열화 모니터링 방법

본 논문에서는 네트워크 전송 중에 패킷손실에 의해 열화되는 IPTV 셋탑박스(STB: Set-Top Box) 재생영상의 화질을 모니터링하는 방법인 VR-VQMS(Visual Rhythm based Video Quality Monitoring Scheme)를 제안한다. 제안된 방법은 인코딩된 영상의 비쥬얼리듬(VR) 정보와 STB 재생영상의 비쥬얼리듬 정보를 특징정보로 사용하는 감소기준법 기반의 영상품질 측정 방법이며, 특히 영상품질관리 측면에서 패킷손실에 의한 화질열화를 정량화한 것이다. 화면열화 정도를 측정하기 위해 기존의 PSNR(peak-to-peak signal-to-noise ratio)을 수정한 NPSNR(Networked PSNR)이라는 새로운 품질척도를 정의하고 이를 기반으로 객관적 및 주관적 열화 척도를 정의하였으며, 온라인 VR-VQMS와 오프라인 VR-VQMS 두 가지의 화질열화 모니터링 방식을 제안하였다. 제안된 방법을 사용하면 화질열화가 발생된 시간과 지속시간, 발생횟수, 평균열화량 등에 대한 통계정보를 얻을 수 있을 뿐만 아니라 시청자의 주관적 체감화질까지 측정할 수 있으며, VR 정보를 사용하기 때문에 눈으로도 직접 이들을 확인할 수 있다는 장점이 있다. 모의실험 결과를 통하여 VR 정보로 추정한 NPSNR 을 이용하여 계산한 화질열화 척도값이 2D 재생영상에 대해 계산한 화질열화 척도값을 매우 근사적으로 추정하고, 시청자의 주관적 화질까지도 상당히 정확하게 추정함을 확인할 수 있었다. 제안된 방법은 IPTV 사업에서 개별 고객들의 시청품질을 평가하는 품질관리 솔루션의 일부로 적용가능하며, 복잡한 연산은 품질관리서버가 수행하고, STB는 작고 가벼운 에이전트 프로그램만 탑재하면 되기 때문에 실용성이 뛰어난 장점이 있다. In this paper, we propose a novel video quality degradation monitoring scheme titled VR-VQMS(Visual Rhythm based Video Quality Monitoring Scheme) over an IPTV service prone to packet losses during network transmission. Proposed scheme quantifies the amount of quality degradation due to packet losses, and can be classified into a RR(reduced-reference) based quality measurement scheme exploiting visual rhythm data of H.264-encoded video frames at a media server and reconstructed ones at an Set-top Box as feature information. Two scenarios, On-line and Off-line VR-VQMS, are proposed as the practical solutions. We define the NPSNR(Networked Peak-to-peak Signal-to-Noise Ratio) modified by the well-known PSNR as a new objective quality metric, and several additional objective and subjective metrics based on it to obtain the statistics on timing, duration, occurrence, and amount of quality degradation. Simulation results show that the proposed method closely approximates the results from 2D video frames and gives good estimation of subjective quality(i.e.,MOS(mean opinion score)) performed by 10 test observers. We expect that the proposed scheme can play a role as a practical solution to monitor the video quality experienced by individual customers in a commercial IPTV service, and be implemented as a small and light agent program running on a resource-limited set-top box.

AIG based nonlinear anisotropic smoothing strategy for vector-valued images

The effects of the Rician noise on the calculated tensors are analyzed and an affine invariant gradient (AIG) based nonlinear anisotropic smoothing strategy is presented. The AIG based smoothing strategy is a development of the affine invariant nonlinear anisotropic diffusion (AINAD) restoration model, introduced by Guillermo Sapiro, and adopted to restore vector-valued data. To evaluate the efficiency of the presented AINAD model in accounting for the Rician noise introduced into the vector-valued data, the peak-to-peak signal-to-noise ratio (PSNR), signal-to-mean squared error ratio (SMSE) and Beta(parameter that stands for edge preservation) metrics are used. The experiment results acquired from the synthetic and real data prove the good performance of the presented filter.

비쥬얼리듬을 이용한 IPTV Set-top Box 재생영상에 대한 PSNR 추정 기법

본 논문에서는 PSNR(Peak-to-peak Signal to Noise Ratio)이 다른 어떤 척도보다도 화질의 평가에 있어서 중요하다는 전제하에 비쥬얼리듬(VR: Visual Rhythm) 정보를 특징정보로 이용하여 원본영상이 가용하지 않은 일반 시청자들의 STB(Set-Top Box) 재생영상으로부터 PSNR을 추정하는 방법을 제안하였다. 핵심 아이디어는 VR정보는 근사적으로 2차원 영상프레임을 1차원으로 투영한 정보라 할 수 있기 때문에 수직성분의 화소값들로만 이루어져 있을지라도 화면특성에 관한 상당한 정보를 가지고 있기 때문에 송수신단의 VR정보만을 이용하여 PSNR값을 추정하는 것이 가능하다는 것이다. 모의실험을 통해 VR정보로부터 추정한 PSNR값이 매 프레임 별로 2차원 재생영상으로부터 구한 PSNR을 근사적으로 추정함으로써 시청자들이 평균적으로 어느 정도의 화질로 시청하고 있는지 비교적 정확하게 모니터링이 가능하다는 것을 확인하였다. 제안된 방법은 심각한 화면열화가 발생한 시간 위치와 지속시간, 발생횟수 등의 통계정보뿐 아니라 이들을 시각적으로 직접 확인할 수 있으며, 자원이 부족한 STB의 연산부담을 최소화하면서 효율적으로 영상품질을 모니터링할 수 있는 장점이 있다. In this paper, we propose a PSNR(peak-to-peak signal to noise ratio) estimation method exploiting visual rhythm information for the reconstructed video frames at the customer's STB(Set-top Box). Key idea is that we can estimate the PSNR by using VR(visual rhythm) information even though a VR consists of the pixels in a vertical direction of a 2D(2-dimensional) video frame, because VR is the 1D projected version of a 2D video frame approximately. Simulation results show that the estimated PSNR from VR information is closely related to the PSNR from 2D video frames. The advantages of the proposed scheme includes that it can monitor the video quality efficiently while minimizing the computation load of STB, and show the location, duration and occurrence count of severe picture degradation.

Compression of ray information in three-dimensional integral imaging

We present a method to compress ray information in three-dimensional (3-D) integral imaging (II) using the Karhunen-Loeve transform (KLT). Elemental images in II are highly correlated because they are picked-up by numerous lenslets, thus, the KLT can compress an integral image more effectively than 2-D ordinary images. In the hybrid coding scheme, the KLT is distinctly applied to disjoint subsets which are partitioned by a vector quantization (VQ). In the optical experiments, we show that 3-D image quality degradation is negligible if approximately 10% of eigenvectors with the largest eigenvalues are used. We also evaluate the quality of uncompressed images by signal-to-noise ratio (SNR) and peak-to-peak signal-to-noise ratio (PSNR). The presented coding scheme is shown to be better than JPEG (Joint Photographic Experts Group) for the lower bit rates in the II compression. Optical and numerical experiments are presented.

JPEG dequantization array for regularized decompression

An optimized dequantization array is proposed for the Joint Photographers Expert Group (JPEG) scheme. The reconstruction error is minimized subjected to a regularity constraint. Both the peak-to-peak signal-to-noise ratio (PPSNR) and the visual quality are improved. In addition, the method reduces discontinuities in postprocessing such as edge detection. This approach is in full compliance with the JPEG standard.

Motion-compensated wavelet transform coder for very low bit-rate visual telephony

Abstract This paper proposes a new motion-compensated wavelet transform video coder for very low bit-rate visual telephony. The proposed coder sequentially employs: (1) selective motion estimation on the wavelet transform domain, (2) motion-compensated prediction (MCP) of wavelet coefficients, and (3) selective entropy-constrained vector quantization (ECVQ) of the resultant MCP errors. The selective schemes in motion estimation and in quantization, which efficiently exploit the characteristic of image sequences in a visual telephony, considerably reduce the computational burden. The coder also employs a tree structure encoding to represent efficiently which blocks were encoded. In addition, in order to reduce the number of ECVQ codebooks and the image dependency of their performance, we introduce a preprocessing of signals which normalizes input vectors of ECVQ. Simulation results show that our video coder provides good PSNR (peak-to-peak signal-to-noise ratio) performance and efficient rate control.

Wavelet transform coding for MCP error images using quadtree decomposition and ECVQ

Abstract This paper proposes a new wavelet transform video coder which employs motion compensation, wavelet decomposition, and entropy-constrained vector quantization (ECVQ), in sequence. Each of layered subimages obtained from wavelet decomposition is segmented into basic blocks, and then the blocks are selectively encoded by ECVQ according to the energy of the samples. We introduce an efficient method to encode the map representing which blocks are encoded, based on inter-band prediction followed by a quadtree encoding. The proposed coder uses a simple forward analyzer in order to optimize the encoding parameters and introduces a preprocessing of signals which normalizes the input vectors of ECVQ in order to reduce the image-dependency of ECVQ codebooks. Simulation results show that our video coder provides good PSNR (peak-to-peak signal-to-noise ratio) performance and efficient rate control.