Denoising imaging polarimetry by adapted BM3D method.

In addition to the visual information contained in intensity and color, imaging polarimetry allows visual information to be extracted from the polarization of light. However, a major challenge of imaging polarimetry is image degradation due to noise. This paper investigates the mitigation of noise through denoising algorithms and compares existing denoising algorithms with a new method, based on BM3D (Block Matching 3D). This algorithm, Polarization-BM3D (PBM3D), gives visual quality superior to the state of the art across all images and noise standard deviations tested. We show that denoising polarization images using PBM3D allows the degree of polarization to be more accurately calculated by comparing it with spectral polarimetry measurements.

BVI-VFI: A Video Quality Database for Video Frame Interpolation.

Video frame interpolation (VFI) is a fundamental research topic in video processing, which is currently attracting increased attention across the research community. While the development of more advanced VFI algorithms has been extensively researched, there remains little understanding of how humans perceive the quality of interpolated content and how well existing objective quality assessment methods perform when measuring the perceived quality. In order to narrow this research gap, we have developed a new video quality database named BVI-VFI, which contains 540 distorted sequences generated by applying five commonly used VFI algorithms to 36 diverse source videos with various spatial resolutions and frame rates. We collected more than 10,800 quality ratings for these videos through a large scale subjective study involving 189 human subjects. Based on the collected subjective scores, we further analysed the influence of VFI algorithms and frame rates on the perceptual quality of interpolated videos. Moreover, we benchmarked the performance of 33 classic and state-of-the-art objective image/video quality metrics on the new database, and demonstrated the urgent requirement for more accurate bespoke quality assessment methods for VFI. To facilitate further research in this area, we have made BVI-VFI publicly available at https://github.com/danier97/BVI-VFI-database.

Fixation Prediction and Visual Priority Maps for Biped Locomotion.

This paper presents an analysis of the low-level features and key spatial points used by humans during locomotion over diverse types of terrain. Although, a number of methods for creating saliency maps and task-dependent approaches have been proposed to estimate the areas of an image that attract human attention, none of these can straightforwardly be applied to sequences captured during locomotion, which contain dynamic content derived from a moving viewpoint. We used a novel learning-based method for creating a visual priority map informed by human eye tracking data. Our proposed priority map is created based on two fixation types: first exploiting the observation that humans search for safe foot placement and second that they observe the edges of a path as a guide to safe traversal of the terrain. Texture features and the difference between them, observed at the region around an eye position, are employed within a support vector machine to create a visual priority map for biped locomotion. The results show that our proposed method outperforms the state-of-the-art, particularly for more complex terrains, where achieving smooth locomotion needs more attention on the traversing path.

Combined morphological-spectral unsupervised image segmentation.

The goal of segmentation is to partition an image into disjoint regions, in a manner consistent with human perception of the content. For unsupervised segmentation of general images, however, there is the competing requirement not to make prior assumptions about the scene. Here, a two-stage method for general image segmentation is proposed, which is capable of processing both textured and nontextured objects in a meaningful fashion. The first stage extracts texture features from the subbands of the dual-tree complex wavelet transform. Oriented median filtering is employed, to circumvent the problem of texture feature response at step edges in the image. From the processed feature images, a perceptual gradient function is synthesised, whose watershed transform provides an initial segmentation. The second stage of the algorithm groups together these primitive regions into meaningful objects. To achieve this, a novel spectral clustering technique is proposed, which introduces the weighted mean cut cost function for graph partitioning. The ability of the proposed algorithm to generalize across a variety of image types is demonstrated.

Image segmentation using a texture gradient based watershed transform.

The segmentation of images into meaningful and homogenous regions is a key method for image analysis within applications such as content based retrieval. The watershed transform is a well established tool for the segmentation of images. However, watershed segmentation is often not effective for textured image regions that are perceptually homogeneous. In order to segment such regions properly, the concept of the "texture gradient" is introduced. Texture information and its gradient are extracted using a novel nondecimated form of a complex wavelet transform. A novel marker location algorithm is subsequently used to locate significant homogeneous textured or non textured regions. A marker driven watershed transform is then used to segment the identified regions properly. The combined algorithm produces effective texture and intensity based segmentation for application to content based image retrieval.

Atmospheric turbulence mitigation using complex wavelet-based fusion.

Restoring a scene distorted by atmospheric turbulence is a challenging problem in video surveillance. The effect, caused by random, spatially varying, perturbations, makes a model-based solution difficult and in most cases, impractical. In this paper, we propose a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI). In order to extract accurate detail about objects behind the distorting layer, a simple and efficient frame selection method is proposed to select informative ROIs only from good-quality frames. The ROIs in each frame are then registered to further reduce offsets and distortions. We solve the space-varying distortion problem using region-level fusion based on the dual tree complex wavelet transform. Finally, contrast enhancement is applied. We further propose a learning-based metric specifically for image quality assessment in the presence of atmospheric distortion. This is capable of estimating quality in both full- and no-reference scenarios. The proposed method is shown to significantly outperform existing methods, providing enhanced situational awareness in a range of surveillance scenarios.

Gaze location prediction for broadcast football video.

The sensitivity of the human visual system decreases dramatically with increasing distance from the fixation location in a video frame. Accurate prediction of a viewer's gaze location has the potential to improve bit allocation, rate control, error resilience, and quality evaluation in video compression. Commercially, delivery of football video content is of great interest because of the very high number of consumers. In this paper, we propose a gaze location prediction system for high definition broadcast football video. The proposed system uses knowledge about the context, extracted through analysis of a gaze tracking study that we performed, to build a suitable prior map. We further classify the complex context into different categories through shot classification thus allowing our model to prelearn the task pertinence of each object category and build the prior map automatically. We thus avoid the limitation of assigning the viewers a specific task, allowing our gaze prediction system to work under free-viewing conditions. Bayesian integration of bottom-up features and top-down priors is finally applied to predict the gaze locations. Results show that the prediction performance of the proposed model is better than that of other top-down models that we adapted to this context.

Rate-distortion optimised video transmission using pyramid vector quantisation.

Conventional video compression relies on interframe prediction (motion estimation), intra frame prediction and variable-length entropy encoding to achieve high compression ratios but, as a consequence, produces an encoded bitstream that is inherently sensitive to channel errors. In order to ensure reliable delivery over lossy channels, it is necessary to invoke various additional error detection and correction methods. In contrast, techniques such as Pyramid Vector Quantisation have the ability to prevent error propagation through the use of fixed length codewords. This paper introduces an efficient rate distortion optimisation algorithm for intra-mode PVQ which offers similar compression performance to intra H.264/AVC and Motion JPEG 2000 while offering inherent error resilience. The performance of our enhanced codec is evaluated for HD content in the context of a realistic (IEEE 802.11n) wireless environment. We show that PVQ provides high tolerance to corrupted data compared to the state of the art while obviating the need for complex encoding tools.

Retinal image registration based on multiscale products and optic disc detection.

This paper presents a new approach to segmentation-driven retinal image registration. The proposed algorithm aims to help physicians to detect changes that occur in the blood vasculature due to various diseases. The proposed approach uses multiscale products, which augment the difference between blood vessels and the rest of the retina. The result of scale multiplication is then iteratively thresholded in order to obtain a binary map of vessels inside the retina. For the registration part, the centre of the optic disc is detected and used as control point. Having determined both the position of the blood vessels and the centre of the optic disc, translational and rotational differences between the images can be eliminated and registration can be achieved. The centroid of the optic disc is used as the center of rotation. The final registration is then achieved by searching the best match between the two images using a XOR operation.