Multiresolution-based Texture Adaptive Motion Detection for De-interlacing

A same-parity 4-field based motion adaptive deinterlacing algorithm combining with texture detection is presented in this paper. Comparing with the existing low-end deinterlacing algorithm, this method greatly improves image quality for both static and fast moving objects due to high motion detection accuracy. Its hardware architecture is also simpler than high-end deinterlacer architecture, e.g. motion estimation & compensation deinterlacer. Its FPGA implementation is also presented.

This study tested the effectiveness of the Bird's Eye View (BEV) technique in documentaries themed on anthropogenic marine environmental damage to improve the quality of visual narrative and audience spatial perception. Using a mixed methods approach with a quasi-experimental design (Posttest-Only Control Group Design), 40 students of Universitas Padjadjaran participated, divided into an experimental group (watching a movie with BEV) and a control group (without BEV). Data collection was carried out through a Likert scale questionnaire and interviews, then analyzed using the Mann-Whitney U test. Results showed significant differences (p=0.014 < 0.05) between the two groups, where the experimental group showed better quality of visual narrative and spatial perception. These findings confirm the effectiveness of BEV techniques in communicating the scale and complexity of environmental issues visually and spatially. This research contributes to the development of documentary film narratives and environmental communication literature, with practical implications for filmmakers to utilize BEVs to enhance the visual impact and spatial perception of audiences in environmental education.

Multifocal lenses are increasingly used solutions for presbyopia, the age-related loss of crystalline lens focus ability. These lenses work by the principle of simultaneous vision, superimposing focused and defocused images on the retina. Providing the experience of simultaneous vision to a patient before permanent implantation of a multifocal lens is a recognized unmet need to increase the patient’s confidence and optimize the lens selection. We developed a hand-held, see-through multifocal vision simulator based on temporal multiplexing of a tunable lens. The device was calibrated and validated using focimetry and Hartmann–Shack aberrometry revealing high reproducibility of the through-focus multifocal energy distribution and high optical quality. We measured visual acuity and perceptual quality on nine cyclopeged patients with three monofocal, two bifocal, and two trifocal corrections with different far/intermediate/near energy distributions simulated using the device. Visual performance and perceptual quality with multifocal corrections varied across patients, although they were more uniform across distances than monofocal corrections. Among the bifocal and trifocal designs, a trifocal with more energy at far was the most frequently identified as providing better quality. The simultaneous vision simulator proved a promising compact tool to study visual performance with multifocal corrections and to select the lens design best suited for each patient, alternative to costly and bulky adaptive optics based devices.

Perceptual quality metrics are widely deployed in image and video processing systems. These metrics aim to emulate the integral mechanisms of the human visual system (HVS) to correlate well with visual perception of quality. One integral property of the HVS is, however, often neglected: visual attention (VA) [1]. The essential mechanisms associated with VA consist mainly of higher cognitive processing, deployed to reduce the complexity of scene analysis. For this purpose, a subset of the visual information is selected by shifting the focus of attention across the visual scene to the most relevant objects. By neglecting VA, perceptual quality models inherently assume that all objects draw the attention of the viewer to the same degree.

Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.g., luminance, contrast, and gradient) do not seem to be consistent with human perception, and the effects pooling is often done in ad-hoc ways. In this paper, we propose a novel full-reference IQA metric. It applies non-negative matrix factorization (NMF) to measure image degradations by making use of the parts-based representation of NMF. On the other hand, a new machine learning technique [extreme learning machine (ELM)] is employed to address the limitations of the existing pooling techniques. Compared with neural networks and support vector regression, ELM can achieve higher learning accuracy with faster learning speed. Extensive experimental results demonstrate that the proposed metric has better performance and lower computational complexity in comparison with the relevant state-of-the-art approaches.

Soil phosphorus (P) availability in lowland tropical rainforests influences the distribution and growth of tropical tree species. Determining the P-acquisition strategies of tropical tree species could therefore yield insight into patterns of tree β-diversity across edaphic gradients. In particular, the synthesis of root phosphatases is likely to be of significance given that organic P represents a large pool of potentially available P in tropical forest soils. It has also been suggested that a high root phosphatase activity in putative nitrogen (N) -fixing legumes might explain their high abundance in lowland neotropical forests under low P supply. Here, we measured phosphomonoesterase (PME) activity on the first three root orders of co-occurring tropical tree species differing in their N-fixation capacity, growing on soils of contrasting P availability in Panama. Our results show that root PME activity was higher on average in P-poor than in P-rich soils, but that local variation in PME activity among co-occurring species within a site was larger than that explained by differences in soil P across sites. Legumes expressed higher PME activity than nonlegumes, but nodulated legumes (i.e., actively fixing nitrogen) did not differ from legumes without nodules, indicating that PME activity is unrelated to N fixation. Finally, PME activity declined with increasing root order, but the magnitude of the decline varied markedly among species, highlighting the importance of classifying fine roots into functional groups prior to measuring root traits. Our results support the hypothesis that low-P promotes a high root PME activity, although the high local variation in this trait among co-occurring species points toward a high functional diversity in P-acquisition strategies within an individual community.

Work presented in the paper includes two parts: first we measured the detectability and annoyance of frame dropping's effect on perceptual visual quality evaluation under different motion and framesize conditions. Then, a new logistics function and an effective yet simple motion content representation are selected to model the relationship among motion, framerate and negative impact of frame-dropping on visual quality, in one formula. The high Pearson and Spearman correlation results between the MOS and predicted MOSp, as well as the results of other two error metrics, confirm the success of the selected logistic function and motion content representation.

The complexity and challenging underwater environment leading to degradation in underwater image. Measuring the quality of underwater image is a significant step for the subsequent image processing step. Existing Image Quality Assessment (IQA) methods do not fully consider the characteristics of degradation in underwater images, which limits their performance in underwater image assessment. To address this problem, an Underwater IQA (UIQA) method based on color space multi-feature fusion is proposed to focus on underwater image. The proposed method converts underwater images from RGB color space to CIELab color space, which has a higher correlation to human subjective perception of underwater visual quality. The proposed method extract histogram features, morphological features, and moment statistics from luminance and color components and concatenate the features to obtain fusion features to better quantify the degradation in underwater image quality. After features extraction, support vector regression(SVR) is employed to learn the relationship between fusion features and image quality scores, and gain the quality prediction model. Experimental results on the SAUD dataset and UIED dataset show that our proposed method can perform well in underwater image quality assessment. The performance comparisons on LIVE dataset, TID2013 dataset,LIVEMD dataset,LIVEC dataset and SIQAD dataset demonstrate the applicability of the proposed method.

Image quality assessment (IQA) is very important for both end-users and service providers since a high-quality image can significantly improve the user's quality of experience (QoE) and also benefit lots of computer vision algorithms. Most existing blind image quality assessment (BIQA) models were developed for synthetically distorted images, however, they perform poorly on in-the-wild images, which are widely existed in various practical applications. In this paper, we propose a novel BIQA model for in-the-wild images by addressing two critical problems in this field: how to learn better quality-aware feature representation, and how to solve the problem of insufficient training samples in terms of their content and distortion diversity. Considering that perceptual visual quality is affected by both low-level visual features (e.g. distortions) and high-level semantic information (e.g. content), we first propose a staircase structure to hierarchically integrate the features from intermediate layers into the final feature representation, which enables the model to make full use of visual information from low-level to high-level. Then an iterative mixed database training (IMDT) strategy is proposed to train the BIQA model on multiple databases simultaneously, so the model can benefit from the increase in both training samples and image content and distortion diversity and can learn a more general feature representation. Experimental results show that the proposed model outperforms other state-of-the-art BIQA models on six in-the-wild IQA databases by a large margin. Moreover, the proposed model shows an excellent performance in the cross-database evaluation experiments, which further demonstrates that the learned feature representation is robust to images with diverse distortions and content. The code is available at https://github.com/sunwei925/StairIQA.

Objective Video Quality Assessment (VQA) is often used to predict users visual perception of video quality. In the literature, the performance evaluation of objective measures is based on benchmark subjective scores of perceived quality. This paper shows the evaluation of an algorithmic measure on videos presented on mobile devices. The VQA measure is called Sencogi Spatio-Temporal Saliency Metric (Sencogi-STSM), and it uses a spatio-temporal saliency to model subjective perception of video quality. Since STSM was previously validated with a subjective test conducted on laptop computers, the goal of this work was to verify whether the measure is able to significantly predict users’ perception of video quality also on mobile devices. Results show that, compared to the standard VQA metrics, only Sencogi-STSM is able to significantly predict subjective DMOS. This paper describes Sencogi-STSM’s biologically plausible model, its performance evaluation and the comparison with the most commonly used objective VQA metrics.

Lens flare is a common nighttime artifact caused by strong light sources scattering within camera lenses, leading to hazy streaks, halos, and glare that degrade visual quality. However, existing methods usually fail to effectively address nonuniform scattered flares, which severely reduces their applicability to complex real-world scenarios with diverse lighting conditions.To address this issue, we propose SLCFormer, a novel spectral-local context transformer framework for effective nighttime lens flare removal. SLCFormer integrates two key modules: the Frequency Fourier and Excitation Module (FFEM), which captures efficient global contextual representations in the frequency domain to model flare characteristics, and the Directionally-Enhanced Spatial Module (DESM) for local structural enhancement and directional features in the spatial domain for precise flare removal. Furthermore, we introduce a ZernikeVAE-based scatter flare generation pipeline to synthesize physically realistic scatter flares with spatially varying PSFs, bridging optical physics and data-driven training. Extensive experiments on the Flare7K++ dataset demonstrate that our method achieves state-of-the-art performance, outperforming existing approaches in both quantitative metrics and perceptual visual quality, and generalizing robustly to real nighttime scenes with complex flare artifacts.

In the conventional video processing architecture, it is the responsibility of video decoder to decode the raw picture out while it is the responsibility of video post-processor to do operations such as de-interlacing, de-blocking, alpha-blending, as well as the 3:2 pull down detection in DVD playback solution. Our solution mainly focuses on MPEG-2 video decoding, but it can be used more widely in other video format. Conventionally, the video decoder merely provides useful guidance to the decoded picture consumer, the video post-processor. Normally, decisions are made by video post-processor itself to select de-interlacing methods, such as BOB, WAVE and motion adaptive de-interlacing. This paper introduces an interactive method between video decoder and video post-processor. Before providing decoded frame to video post-processor, the video decoder has make decision upon de-interlacing method by analyzing three or more frames in the frame buffers. This method is proved to be memory saving.

Accurate inverse-consistent symmetric optical flow for 4D CT lung registration

Projection (or dimensionality reduction) techniques have been used as a means to handling the growing dimensionality of data sets as well as providing a way to visualize information coded into point relationships. Their role is essential in data interpretation and simultaneous use of different projections and their visualizations improve data understanding and increase the level of confidence in the result. For that purpose, projections should be fast to allow multiple views of the same data set. In this work we present a novel fast technique for projecting multi-dimensional data sets into bidimensional (2D) spaces that preserves neighborhood relationships. Additionally, a new technique for improving 2D projections from multi-dimensional data is presented, that helps reduce the inherent loss of information yielded by dimensionality reduction. The results are stimulating and are presented in the form of comparative visualizations against known and new 2D projection techniques. Based on the projection improvement approach presented here, a new metric for quality of projection is also given, that matches well the visual perception of quality. We discuss the implication of using improved projections in visual exploration of large data sets and the role of interaction in visualization of projected subspaces.

A reliable video quality assessment (VQA) algorithm is essential for evaluating and optimizing video processing pipelines. In this paper, we propose a quality aggregation network (QAN) for full-reference VQA, which models the characteristics of human visual perception of video quality in both spatial and temporal domain. The proposed QAN is composed of two mod-ules, the spatial quality aggregation (SQA) network and the tem-poral quality aggregation (TQA) network. Specifically, the SQA network models the quality of video frames using 3D CNN, taking both spatial and temporal masking effects into consideration for the modeling of the perception of human visual system (HVS). In the TQA network, considering the memory effect of HVS facing the temporal variation of frame-level quality, an LSTM-based temporal quality pooling network is proposed to capture the nonlinearities and temporal dependencies involved in the process of quality evaluation. According to the experimental results on two well-established VQA databases, the proposed model could outperform the state-of-the-art metrics. The code of the proposed method is available at: https://github.com/lorenzowu/QAN.