Aliasing artefact index for image interpolation quality assessment

A preliminary study of a non-reference aliasing artefact index (AAI) metric is presented in this paper. We focus on the effects of combining a full-reference metric and interpolation algorithm. The nearest neighbor algorithm (NN) is used as the gold standard against which test-algorithms are judged in terms of aliased structures. The structural similarity index (SSIM) metric is used to evaluate a test image (i.e. a test-algorithm's image) and a reference image (i.e. the NN's image). Preliminary experiments demonstrated promising effects of the AAI metric against state-of-the-art non-reference metrics mentioned. A new study may further develop the studied metric for potential applications in image quality adaptation and/or monitoring in medical imaging

Effects of rescaling bilinear interpolant on image interpolation quality

Rescaling bilinear (RB) interpolant's pixels is a novel image interpolation scheme. In the current study, we investigate the effects on the quality of interpolated images. RB determines the lower and upper bounds using the standard deviation of the four nearest pixels to find the new interval or range that will be used to rescale the bilinear interpolant's pixels. The products of the rescaled-pixels and corresponding distance-based-weights are added to estimate the new pixel value, to be assigned at the empty locations of the destination image. Effects of RB on image interpolation quality were investigated using standard full-reference and non-reference objective image quality metrics, particularly those focusing on interpolated images features and distortion similarities. Furthermore, variance and mean based metrics were also employed to further investigate the effects in terms of contrast and intensity increment or decrement. The Matlab based simulations demonstrated generally superior performances of RB compared to the traditional bilinear (TB) interpolation algorithm. The studied scheme's major drawback was a higher processing time and tendency to rely on the image type and/or specific interpolation scaling ratio to achieve superior performances. Potential applications of rescaling based bilinear interpolation may also include ultrasound scan conversion in cardiac ultrasound, endoscopic ultrasound, etc

DERMATOLOGICAL IMAGE DENOISING USING ADAPTIVE HENLM METHOD

In this paper we propose automatic image denoising method based on Hermite functions (HeNLM). It is an extension of non-local means (NLM) algorithm. Differences between small image blocks (patches) are replaced by differences between feature vectors thus reducing computational complexity. The features are calculated in coordinate system connected with image gradient and are invariant to patch rotation. HeNLM method depends on the parameter that controls filtering strength. To chose automatically this parameter we use a no-reference denoising quality assessment method. It is based on Hessian matrix analysis. We compare the proposed method with full-reference methods using PSNR metrics, SSIM metrics, and its modifications MSSIM and CMSC. Image databases TID, DRIVE, BSD, and a set of dermatological immunofluorescence microscopy images were used for the tests. It was found that more perceptual CMSC and MSSIM metrics give worse correspondence than SSIM and PSNR to the results of information preservation by the non-reference image denoising

Logarithmical hopping encoding: a low computational complexity algorithm for image compression

LHE (logarithmical hopping encoding) is a computationally efficient image compression algorithm that exploits the Weber–Fechner law to encode the error between colour component predictions and the actual value of such components. More concretely, for each pixel, luminance and chrominance predictions are calculated as a function of the surrounding pixels and then the error between the predictions and the actual values are logarithmically quantised. The main advantage of LHE is that although it is capable of achieving a low-bit rate encoding with high quality results in terms of peak signal-to-noise ratio (PSNR) and image quality metrics with full-reference (FSIM) and non-reference (blind/referenceless image spatial quality evaluator), its time complexity is O( n) and its memory complexity is O(1). Furthermore, an enhanced version of the algorithm is proposed, where the output codes provided by the logarithmical quantiser are used in a pre-processing stage to estimate the perceptual relevance of the image blocks. This allows the algorithm to downsample the blocks with low perceptual relevance, thus improving the compression rate. The performance of LHE is especially remarkable when the bit per pixel rate is low, showing much better quality, in terms of PSNR and FSIM, than JPEG and slightly lower quality than JPEG-2000 but being more computationally efficient

A Non-Reference Evaluation of Underwater Image Enhancement Methods Using a New Underwater Image Dataset

The rise of vision-based environmental, marine, and oceanic exploration research highlights the need for supporting underwater image enhancement techniques to help mitigate water effects on images such as blurriness, low color contrast, and poor quality. This paper presents an evaluation of common underwater image enhancement techniques using a new underwater image dataset. The collected dataset is comprised of 100 images of aquatic plants taken at a shallow depth of up to three meters from three different locations in the Great Lake Superior, USA, via a Remotely Operated Vehicle (ROV) equipped with a high-definition RGB camera. In particular, we use our dataset to benchmark nine state-of-the-art image enhancement models at three different depths using a set of common non-reference image quality evaluation metrics. Then we provide a comparative analysis of the performance of the selected models at different depths and highlight the most prevalent ones. The obtained results show that the selected image enhancement models are capable of producing considerably better-quality images with some models performing better than others at certain depths

Full-reference stereoscopic video quality assessment using a motion sensitive HVS model

Stereoscopic video quality assessment has become a major research topic in recent years. Existing stereoscopic video quality metrics are predominantly based on stereoscopic image quality metrics extended to the time domain via for example temporal pooling. These approaches do not explicitly consider the motion sensitivity of the Human Visual System (HVS). To address this limitation, this paper introduces a novel HVS model inspired by physiological findings characterising the motion sensitive response of complex cells in the primary visual cortex (V1 area). The proposed HVS model generalises previous HVS models, which characterised the behaviour of simple and complex cells but ignored motion sensitivity, by estimating optical flow to measure scene velocity at different scales and orientations. The local motion characteristics (direction and amplitude) are used to modulate the output of complex cells. The model is applied to develop a new type of full-reference stereoscopic video quality metrics which uniquely combine non-motion sensitive and motion sensitive energy terms to mimic the response of the HVS. A tailored two-stage multi-variate stepwise regression algorithm is introduced to determine the optimal contribution of each energy term. The two proposed stereoscopic video quality metrics are evaluated on three stereoscopic video datasets. Results indicate that they achieve average correlations with subjective scores of 0.9257 (PLCC), 0.9338 and 0.9120 (SRCC), 0.8622 and 0.8306 (KRCC), and outperform previous stereoscopic video quality metrics including other recent HVS-based metrics

Analysis of anisotropic blind image quality assessment

Understanding quality of an image is a challenging task in absence of good quality reference image in many applications. In degraded image it is often assume that structure of image remains same so the objective of Blind Image Quality Assessment (BIQI) is to detect the structural degradation which is orientation dependent so blind quality of an image can be analyzed through anisotropic measure of an image. This thesis analyzes one of such Blind Image Quality Index (BIQI) measure like Anisotropic Blind Quality Index (ABQI). ABQI is measured by calculating standard deviation using Renyi entropy and directional pseudo wigner distribution. A standard database, Laboratory for Image & Video Engineering (LIVE) database is used to analyse the ABQI algorithm. The algorithm is validated by Spearman and Pearson correlation coefficients. The result provides a way of identifying best quality and noise free images from other degraded versions, allowing an automatic and non-reference classification of images according to their relative quality. It is also shown that the anisotropic measure is well correlated with classical reference metrics such as the Structural Similarity Index Measure (SSIM)

High-Resolution Reference Image Assisted Volumetric Super-Resolution of Cardiac Diffusion Weighted Imaging

Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is the only in vivo method to non-invasively examine the microstructure of the human heart. Current research in DT-CMR aims to improve the understanding of how the cardiac microstructure relates to the macroscopic function of the healthy heart as well as how microstructural dysfunction contributes to disease. To get the final DT-CMR metrics, we need to acquire diffusion weighted images of at least 6 directions. However, due to DWI's low signal-to-noise ratio, the standard voxel size is quite big on the scale for microstructures. In this study, we explored the potential of deep-learning-based methods in improving the image quality volumetrically (x4 in all dimensions). This study proposed a novel framework to enable volumetric super-resolution, with an additional model input of high-resolution b0 DWI. We demonstrated that the additional input could offer higher super-resolved image quality. Going beyond, the model is also able to super-resolve DWIs of unseen b-values, proving the model framework's generalizability for cardiac DWI superresolution. In conclusion, we would then recommend giving the model a high-resolution reference image as an additional input to the low-resolution image for training and inference to guide all super-resolution frameworks for parametric imaging where a reference image is available.Comment: Accepted by SPIE Medical Imaging 202