Emil Dumic

This paper presents a systematic evaluation framework for underwater image enhancement (UIE), focusing on reliable quality assessment for vision applications in challenging underwater environments. The framework jointly analyzes subjective visual quality and objective image quality assessment measures. A controlled, laboratory-based subjective study following the ITU-R absolute category rating protocol is conducted on two datasets: UIEBD (with and without quasi-reference images) and the EUVP validation subset. A total of 132 images from UIEBD and 120 images from EUVP are evaluated, including enhanced images from four recent deep learning-based UIE models (CCL-Net, HUPE, GuidedHybSensUIR, and UDNet). The subjective results reveal dataset-dependent behavior of the evaluated methods, highlighting the challenges of reliable perceptual evaluation in the presence of diverse degradations and quasi-reference data. Objective analysis shows that modern learning-based, no-reference image quality assessment (NR-IQA) models exhibit higher correlation with subjective mean opinion scores than traditional underwater-specific measures. In particular, TOPIQ_NR achieves a Spearman correlation of 0.80 on UIEBD and remains among the top-performing methods on EUVP, where LIQE reaches 0.87, while widely used measures such as UIQM and UCIQE show weaker alignment with human perception. These findings support the adoption of learning-based NR-IQA measures for robust underwater vision systems.

In this paper, we evaluate three AI-generated video models (Sora, CogVideoX and Text2Video-Zero) using both subjective quality assessment and objective quality measures. A dataset of videos is generated based on ten prompts per each model and evaluated by 15 human participants along two perceptual dimensions: overall visual quality and text-to-video alignment. Objective quality is assessed using a wide range of no-reference image, video, and text-to-video alignment measures. Our analysis reveals a significant gap between subjective scores and current objective measures, indicating that existing tools are insufficient for reliably evaluating AI-generated video content. The results emphasize the need for improved objective quality assessment measures and frameworks of AI-generated video sequences. All subjective scores and tested video sequences are publicly available at http://aigvdataset.dynalias.com.

Remarks for the paper: 1. When you propose the method to be used with TIFF images, it is actually not clear which compression you use. Please check TIFF specifications and add this information to the paper. JPEG2000 and H.265/HEVC are image and vide...

This meta-survey provides a comprehensive review of 3D point cloud (PC) applications in remote sensing (RS), essential datasets available for research and development purposes, and state-of-the-art point cloud compression methods. It offers a comprehensive exploration of the diverse applications of point clouds in remote sensing, including specialized tasks within the field, precision agriculture-focused applications, and broader general uses. Furthermore, datasets that are commonly used in remote-sensing-related research and development tasks are surveyed, including urban, outdoor, and indoor environment datasets; vehicle-related datasets; object datasets; agriculture-related datasets; and other more specialized datasets. Due to their importance in practical applications, this article also surveys point cloud compression technologies from widely used tree- and projection-based methods to more recent deep learning (DL)-based technologies. This study synthesizes insights from previous reviews and original research to identify emerging trends, challenges, and opportunities, serving as a valuable resource for advancing the use of point clouds in remote sensing.

This paper presents a face detection and recognition system utilizing a Raspberry Pi computer that is built on a predefined framework. The theoretical section of this article shows several techniques that can be used for face detection, including Haar cascades, Histograms of Oriented Gradients, Support Vector Machine and Deep Learning Methods. The paper also provides examples of some commonly used face recognition techniques, including Fisherfaces, Eigenfaces, Histogram of Local Binary Patterns, SIFT and SURF descriptor-based methods and Deep Learning Methods. The practical aspect of this paper demonstrates use of a Raspberry Pi computer, along with supplementary tools and software, to detect and recognize faces using a pre-defined dataset.

This article describes an empirical exploration on the effect of information loss affecting compressed representations of dynamic point clouds on the subjective quality of the reconstructed point clouds. The study involved compressing a set of test dynamic point clouds using the MPEG V-PCC (Video-based Point Cloud Compression) codec at 5 different levels of compression and applying simulated packet losses with three packet loss rates (0.5%, 1% and 2%) to the V-PCC sub-bitstreams prior to decoding and reconstructing the dynamic point clouds. The recovered dynamic point clouds qualities were then assessed by human observers in experiments conducted at two research laboratories in Croatia and Portugal, to collect MOS (Mean Opinion Score) values. These scores were subject to a set of statistical analyses to measure the degree of correlation of the data from the two laboratories, as well as the degree of correlation between the MOS values and a selection of objective quality measures, while taking into account compression level and packet loss rates. The subjective quality measures considered, all of the full-reference type, included point cloud specific measures, as well as others adapted from image and video quality measures. In the case of image-based quality measures, FSIM (Feature Similarity index), MSE (Mean Squared Error), and SSIM (Structural Similarity index) yielded the highest correlation with subjective scores in both laboratories, while PCQM (Point Cloud Quality Metric) showed the highest correlation among all point cloud-specific objective measures. The study showed that even 0.5% packet loss rates reduce the decoded point clouds subjective quality by more than 1 to 1.5 MOS scale units, pointing out the need to adequately protect the bitstreams against losses. The results also showed that the degradations in V-PCC occupancy and geometry sub-bitstreams have significantly higher (negative) impact on decoded point cloud subjective quality than degradations of the attribute sub-bitstream.