The teaching and learning of the first programming language course, commonly called CS1 (Computer Science 1), is a reported challenge for undergraduate students in different majors and universities. In general, these courses have a high failure rate at institutions around the world. At the same time, there has been an increase in the usage of automated feedback tools for programming language courses. These tools present feedback to help the students know if their code is correct or incorrect while studying. This automatic feedback is important for student learning. In this context, this paper presents the AsPin plugin, developed for the virtual learning environment, which uses the student's profile to provide feedback (messages and advice) to personalize their learning. The automatic feedback helps the student understand where they made mistakes, delivers motivational messages so that the student continues to learn, and points to further academic materials so that the student spends more time studying what they need. This feedback is also personalized for the student's profile, as at the beginning of the course, the student fills out a form with basic information, for example, whether or not they already have programming experience and their major. The plugin was developed for the Moodle platform, a free and open-source virtual learning environment with CodeRunner, an automated grader for multi-programming languages. The AsPin plugin was developed for the CS1 course in Python language by students and instructors from the Department of Computer Science at the University of Brasilia. The plugin presented in this paper was evaluated by undergraduate students taking different majors, which has a high student acceptance rate. This paper presents the applied methodology, the development of the plugin, as well the performance evaluations and perceptions of students from the University of Brasilia in two academic semesters in 2022.

We study the use of predictive approaches alongside the region-adaptive hierarchical transform (RAHT) in attribute compression of dynamic point clouds. The use of intra-frame prediction with RAHT was shown to improve attribute compression performance over pure RAHT and represents the state-of-the-art in attribute compression of point clouds, being part of MPEG's geometry-based test model. We studied a combination of inter-frame and intra-frame prediction for RAHT for the compression of dynamic point clouds. An adaptive zero-motion-vector (ZMV) scheme and an adaptive motion-compensated scheme are developed. The simple adaptive ZMV approach is able to achieve sizable gains over pure RAHT and over the intra-frame predictive RAHT (I-RAHT) for point clouds with little or no motion while ensuring similar compression performance to I-RAHT for point clouds with intense motion. The motion-compensated approach, more complex and more powerful, is able to achieve large gains across all of the tested dynamic point clouds.

Point cloud compression has been studied in standard bodies and we are here concerned with the Moving Picture Experts Group video-based point cloud compression (V-PCC) solution. Plenoptic point clouds (PPC) is a novel volumetric data representation wherein points are associated with colors in all viewing directions to improve realism. It is sampled as a number ($N_{c}$) of attribute colors per point. We propose a new method for the efficient video-based compression of PPC that is backwards compatible with the existing single-color V-PCC decoder. V-PCC generates three image atlases which are encoded using an image/video encoder. We assume there may be a reference color which is to be encoded as the main payload. We generate$N_{c}+3$atlases and we produce$N_{c}$differential images against the reference color image. Those difference images are pixel-wise transformed using an$N_{c}$-point discrete cosine transform, generating$N_{c}$transformed atlases which are encoded, forming the secondary payload. Such secondary information is the plenoptic enhancement to the point cloud. If there is no reference attribute, we skip the differences and use the lowest frequency of the transformed atlases as the main payload. Results are presented that show an unrivaled performance of the proposed method.

Recently finalized, the MPEG Video-based Point Cloud Compression (V-PCC) standard leverages existing video codecs to compress point clouds. This approach relies on existing video coding hardware accelerators to enable its fast adoption. However, the processing steps to project 3D point clouds into 2D frames still have a considerable complexity. Thus, we propose two modifications to TMC2, V-PCC’s test model, considering the memory access pattern: one reducing unnecessary memory allocation and the other increasing data locality through a set of pre-processing steps. Our approach achieved up to 46.31% encoding self-time reduction without changing the resulting bitstream. This paper also provides an analysis of our implementation that may help future V-PCC codecs achieve real-time encoding.

Research Full Paper Most top universities in Brazil are public government institutions and tuition free. However, until recently, access to these institutions has been limited by extremely difficult entrance exams. The high standards at public universities are in contrast to the k-12 educational system, where public schools fail to prepare students for the exams, with only some of the private schools offering adequate preparation. In 2012, the Higher Education System in Brazil changed: the Quota Law was implemented for all 59 federal public government universities. This law reserves 50% of the enrollments for the public high-school students with the best grades in the entrance exams. Also, from this 50% allocation of places for students from the public high-school system, half are allocated to students from low-income families (up to one and a half times the minimum monthly salary), black and indigenous students. In this context, this paper addresses the research question: "How does the intellectual sense of belonging and self-efficacy of the quota students compare to that the non-quota students' taking Introduction to Computer Science courses?" We devised a questionnaire for students enrolled in the first programming course of different majors at a top-10 Brazilian university. This paper presents an analysis of the responses that indicates some differences in self-efficacy perceptions between the students admitted through the quota system and the ones admitted exclusively by their placement in entrance exams.

Point cloud compression presents novel and challenging demands. Video-based solutions can harness existing video codec technology for efficient compression, however, the cloud's 3D geometry and attributes must be presented within a compatible, regular 2D grid. We propose a method for generating equally-sized, square-shaped video blocks containing point cloud color information. Our video blocks may be readily tiled into an image for efficient color compression through a traditional video codec. The focus is on the development of a computationally simple, voxel-to-image projection methodology. Results provide evidence of competitive performance, with average PSNR gains of 0.68dB and attribute (color) bitrate savings of -15.10%, with respect to MPEG's G-PCC (TMC13 v5.1).

Motion estimation in dynamic point cloud analysis or compression is a computationally intensive procedure generally involving a large search space and often complex voxel matching functions. We present an extension and improvement on prior work to speed up block-based motion estimation between temporally adjacent point clouds. We introduce local, or block-based, texture descriptors as a complement to voxel geometry description. Descriptors are organized in an occupancy map which may be efficiently computed and stored. By consulting the map, a point cloud motion estimator may significantly reduce its search space while maintaining prediction distortion at similar quality levels. The proposed texture-based occupancy maps provide significant speedup, an average of 26.9% for the tested data set, with respect to prior work.

Motion estimation is a key component in dynamic point cloud analysis and compression. We present a method for reducing motion estimation computation when processing block-based partitions of temporally adjacent point clouds. We propose the use of an occupancy map containing information regarding size or other higher-order local statistics of the partitions. By consulting the map, the estimator may significantly reduce its search space, avoiding expensive block-matching evaluations. To form the maps we use 3D moment descriptors efficiently computed with one-pass update formulas and stored as scalar-values for multiple, subsequent references. Results show that a speedup of 2 produces a maximum distortion dropoff of less than 2% for the adopted PSNR-based metrics, relative to distortion of predictions attained from full search. Speedups of 5 and 10 are achievable with small average distortion dropoffs, less than 3% and 5%, respectively, for the tested data set.

Light field (LF) imaging captures multiple intensities and directions of light per pixel during acquisition in a 3D scene, so that novel images of different viewpoints or focal points can be synthesized. However, transmitting all LF data before viewer observation incurs a large startup delay. To avoid such delay, we propose a new interactive LF streaming framework, where a client periodically requests viewpoint images, and in response a server synthesizes and transmits each requested image as a carefully chosen sparse linear combination of sub-aperture images. For each received synthesized image, the client “decodes” and recovers a new sub-aperture image using a cache of known sub-aperture images. As the cache of decoded sub-aperture images grows over time, the client becomes capable of synthesizing new view/focal point images, reducing overall transmission cost. Experimental results show that our proposed scheme can deliver synthesized images at high quality, even though only sparse sub-aperture images are used for synthesis. Moreover, compared with a scenario where the requested synthesized images are always transmitted, our proposed scheme achieves a significant reduction in accumulated rate when a sufficient number of images are transmitted.

Virtual view synthesis is a key component of multi-view imaging systems that enable visual immersion environments for emerging applications, e.g., virtual reality and 360-degree video. Using a small collection of captured reference view-points, this technique reconstructs any view of a remote scene of interest navigated by a user, to enhance the perceived immersion experience. We carry out a convexity characterization analysis of the virtual view reconstruction error that is caused by compression of the captured multi-view content. This error is expressed as a function of the virtual viewpoint coordinate relative to the captured reference viewpoints. We derive fundamental insights about the nature of this dependency and formulate a prediction framework that is able to accurately predict the specific dependency shape, convex or concave, for given reference views, multi-view content and compression settings. We are able to integrate our analysis into a proof-of-concept coding framework and demonstrate considerable benefits over a baseline approach.

In a free-viewpoint television network, each viewer chooses its point of view from which to watch a scene. We use the concept of total observed distortion, wherein we aim to minimize the distortion of the view observed by the viewers as opposed to the distortion of each camera, to develop an optimized bit-rate allocation for each camera. Our attention-weighted approach effectively gives more bits to the cameras that are more watched. The more concentrated the viewer distribution, the larger the bit-rate savings, for a given total observed distortion, compared with the uniform rate allocation. We analyze and model the distortion of a synthesized view as a function of the distortions (both in texture and/or depth) of the nearby cameras. Based on such models, we develop optimal rate-allocation methods for texture images, considering a uniform bit allocation for depth, and for both texture and depth simultaneously. Simulation results are shown, demonstrating not only the correctness of the optimized solution, but also measuring its improvement against uniform rate allocation for a few viewer distributions.

We propose optimal rate-allocation, using viewer attention information among viewpoints, for depth map cameras within a free-viewpoint television broadcast system. An attention-weighted rate-allocation framework enables bit-rate, or quality, to be distributed across the multiple cameras in accordance with viewer interest, minimizing total observed distortions perceived among all viewers. Prior work has considered attention-weighted rate-allocation for texture cameras only in such systems. Depth maps, nonetheless, are a common requirement for view rendering in many systems. They may constitute a significant part of overall transmission bit-rate and they present their own distortion characteristics, different from those of texture. We model the effects of depth camera distortions upon virtual or synthesized views and propose an optimal attention-weighted rate-allocation among depth cameras. Results show significant gains in average PSNR of synthesized views and bit-rate savings of our proposal relative to the balanced rate-allocation alternative.

Although images as viewed from intermediate virtual viewpoints can be synthesized using texture and depth maps from nearby camera views via depth-image-based rendering (DIBR), the rendered images contain disocclusion holes - spatial regions that were not visible in the reference views due to foreground object occlusion - that requires proper filling. In this paper, we introduce a new signal prior into the hole filling problem formulation: given disocclusion holes are part of the background and background tends to have low visual saliency, the extrapolated signal into the holes must also be of low saliency. Mathematically, we add a low-saliency prior to an exemplar-based inpainting algorithm, so that the best-matched block has both small matching cost and is of low visual saliency. Moreover, we compute a suitable Lagrange multiplier value for the saliency cost term via analysis of the reference images. Experimental results show that using a low-saliency prior can improve performance by 0.5 dB over a previous hole filling scheme.

We propose a framework for optimal rate-allocation in free-viewpoint television (FVTV) for a general camera arrangement based on the attention the viewers are paying to each camera. In a recent letter [1], the authors proposed a FVTV broadcast architecture and an optimal bit-allocation approach, assuming a uniformly-spaced one-dimensional arrangement of cameras. Quality (or bit-rate) at each camera was determined by viewer attention in order to minimize total observed distortion. Here, we extend the optimized bit-allocation scheme to allow for a more generic camera arrangement in FVTV. We present results on data sets from 1D and 2D array camera setups which show significant overall PSNR gains and bit-rate savings with respect to equally-balanced rate-allocation across cameras.

Free-viewpoint video conferencing allows a participant to observe the remote 3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint image is typically synthesized using two pairs of transmitted texture and depth maps from two neighboring captured viewpoints via depth-image-based rendering (DIBR). To maintain high quality of synthesized images, it is imperative to contain the adverse effects of network packet losses that may arise during texture and depth video transmission. Towards this goal, we develop an integrated approach that exploits the representation redundancy inherent in the multiple streamed videos-a voxel in the 3D scene visible to two captured views is sampled and coded twice in the two views. In particular, at the receiver we first develop an error concealment strategy that adaptively blends corresponding pixels in the two captured views during DIBR, so that pixels from the more reliable transmitted view are weighted more heavily. We then couple it with a sender-side optimization of reference picture selection (RPS) during real-time video coding, so that blocks containing pixel samples of voxels that are visible in both views are more error-resiliently coded in one view only, given adaptive blending will mitigate errors in the other view. Further, synthesized view distortion sensitivities to texture versus depth errors are analyzed, so that relative importance of texture and depth code blocks can be computed for system-wide RPS optimization. Finally, quantization parameter (QP) is adaptively selected per frame, optimally trading off source distortion due to compression with channel distortion due to potential packet losses. Experimental results show that the proposed scheme can outperform previous work by up to 2.9 dB at 5% packet loss rate.

We propose a mixed-resolution coding architecture for stereo color-plus-depth images, where encoding is performed at a low resolution, except for one of the color images. Super-resolution methods are proposed for the depth maps and for the low-resolution color image at the decoder side. Experiments are carried out for several real and synthetic images and reveal a reduction in complexity at the encoder associated with average PSNR gains of the super-resolved low-resolution color image with respect to regular interpolation.

In free viewpoint video, texture and depth maps from two camera-captured viewpoints are transmitted, so that at receiver, a novel virtual view chosen by the client can be synthesized via depth-image-based rendering (DIBR). When irrecoverable packet losses occur during transmission-typically affecting less important spatial regions in the video given unequal error protection (UEP) is deployed-appropriate error concealment strategies must be used at decoder to minimize resulting visual degradation in the synthesized view. Towards this goal, we propose a new optimization framework based on visual saliency to combine two different concealment techniques. First, given a pixel in the virtual view is typically constructed as a convex combination of corresponding pixels in the left and right captured views, weighted pixel blending (WPB) readjusts the weights in the linear sum to reflect the expected error in code blocks that contain the corresponding pixels. Second, exemplar-based patch matching (EPM) finds the most similar patches in the known spatial region to complete missing pixels in the unknown region. To choose between candidates constructed using the two techniques when filling a given pixel patch in the synthesized view, we first compute a weighted sum of expected error and visual saliency for each candidate patch. The candidate with the smaller sum (one with small expected error and visual saliency, so that even if errors do occur, they do not stand out visually) is selected for pixel completion. Experimental results show that our scheme can outperform the use of co-located blocks from a previous frame by up to 0.7dB in PSNR and improve subjective visual quality.

In a free-viewpoint video conferencing system, the viewer can choose any desired viewpoint of the 3D scene for observation. Rendering of images for arbitrarily chosen viewpoint can be achieved through depth-image-based rendering (DIBR), which typically employs “texture-plus-depth” video format for 3D data exchange. Robust and timely transmission of multiple texture and depth maps over bandwidth-constrained and loss-prone networks is a challenging problem. In this paper, we optimize transmission of multiview video in texture-plus-depth format over a lossy channel for free viewpoint synthesis at decoder. In particular, we construct a recursive model to estimate the distortion in synthesized view due to errors in both texture and depth maps, and formulate a rate-distortion optimization problem to select reference pictures for macroblock encoding in H.264 in a computation-efficient way, in order to provide unequal protection to different macroblocks. Results show that the proposed scheme can outperform random insertion of intra refresh blocks by up to 0.73 dB at 5% loss.

In stereoscopic and multiview video, binocular suppression theory states that the visual subjective quality of 3-D experience is not much affected by asymmetrical blurring of the individual views. Based on these studies, mixed-resolution frameworks applied for multiview systems offer great data-size reduction without incurring in significant quality degradation in 3-D video applications. However, it is interesting to recover high-frequency content of the blurred views, to reduce visual strain due to long-term exposure and to make the system suitable for free-viewpoint television. In this paper, we present a novel super-resolution technique, in which low-resolution views are enhanced with the aid of high-frequency content from neighboring full-resolution views, and the corresponding depth information for all views. Occlusions are handled by checking the consistency between views. Tests for synthetic and real image data in stereo and multiview cases are presented, and results show that significant objective quality gains can be achieved without any extra side information.

This paper presents a novel depth map enhancement method which takes as inputs a single view and an initial depth estimate. A region-based framework is introduced wherein a color-based partition of the image is created and depth uncertainty areas are identified according to the alignment of detected depth discontinuities and region borders. A color-based homogeneity criterion is used to guide a constrained region merging process and reconstruct depth estimates within the uncertainty areas. Experimental results on publicly available test sequences illustrate the potential of the algorithm in significantly improving low quality depth estimates.

The joint usage of low- and full-resolution images in multiview systems provides an attractive opportunity for data size reduction while maintaining good quality in 3D applications. In this paper we present a novel application of a super-resolution method for usage within a mixed resolution multiview setup. The technique borrows high-frequency content from neighboring full resolution images to enhance particular low-resolution views. Occlusions are handled through matching of low-resolution images. Both the stereo and the more general multiview cases are considered using the multiview video-plus-depth format. Results demonstrate significant gains in PSNR and in visual quality for test sequences.

This paper presents the trajectory tree as a hierarchical region-based representation for video sequences. Motion, as well as spatial features from multiple frames are used to generate a set of temporal regions structured within a hierarchy of scale and motion coherency. The resulting representations offer a global description of the entire video sequence and enhance semantic analysis potential. A multiscale segmentation strategy is proposed whereby region-merging criteria of progressively greater complexity are used to define partition layers of increasing aptitude for object detection. A novel data structure, called the trajectory adjacency graph, is defined for the long-term analysis of partition sequences. Furthermore, mechanisms for assessing connectivity, verifying temporal continuity, and proposing merging operations based on color, affine, and translational motion homogeneity characteristics over the entire sequence are also introduced. Finally, as demonstrated through experimental results, the trajectory tree offers a concise yet versatile support for video object segmentation, description and retrieval tasks.

Recent video coding strategies, such as H.264/AVC, incorporate an in-loop deblocking filter in order to reduce the effects of quantization noise. These techniques are limited to treating blocky artifacts on smooth regions. In order to solve this, sparsity-based filtering techniques have been recently proposed for efficient filtering of edge and textured areas. More recently, direction-adaptive sparsity-based filtering has also allowed to exploit directional features on intra encoded pictures. This paper proposes a high-performance in-loop filter for deartifacting intra and inter encoded video data. This work extends for Inter frames the use of direction-adaptive sparsity-based filtering techniques and it improves performance by adaptively selecting filtering thresholds consistent with quantization noise statistics, local encoding conditions, compression requirements and the original signal. Thresholds are both spatially and temporally adapted to optimize video quality and/or coding cost. Selected thresholds are encoded and transmitted as side information to the decoder. Experimental results show significant bit rate savings and visual quality enhancement when compared to the state-of-the- art H.264/AVC codec using in-loop deblocking filtering.

This paper describes a hierarchical analysis framework for image sequences. Region merging schemes traditionally used in the construction of partition hierarchies are extended to multiple frames using trajectory merging criteria. The merging criteria assess homogeneity among features throughout the entire sequence to recursively create partitions in the spatio-temporal domain. We propose similarity measures using long-term affine and translational motion features. Furthermore, the analysis of connectivity relations and the algorithm implementation over trajectory adjacency graphs allow the generation of partition sets containing temporally consistent objects characterized by coherent motion. Lastly, we introduce the novel trajectory tree as a single, hierarchical representation of the partitions generated for the complete sequence. Experimental results are provided, illustrating the usefulness of the approach.

This paper describes a technique for generating partition sequences of regions presenting long-term homogeneity in color and motion coherency in terms of affine models. The technique is based on region merging schemes compatible with hierarchical representation frameworks and can be divided into two stages: partition tracking and partition sequence analysis. Partition tracking is a recursive algorithm whereby regions are constructed according to short-term spatio-temporal features, namely color and motion. Partition sequence analysis proposes the trajectory adjacency graph (TAG) to exploit the long-term connectivity relations of tracked regions. A novel trajectory merging strategy using color homogeneity criteria over multiple frames is introduced. Algorithm performance is assessed and comparisons to other proposals are drawn by means of established evaluation metrics.

This paper describes an approach for generating binary partition tree [P. Salembier and L. Garrido, 2000] representations and video object segmentations using a novel region merging strategy based on motion similarity measures of multiple frames of an image sequence. The system operates over color-homogeneous regions, tracked across frames of a shot, representing an over-segmentation of the objects. A long-term motion similarity measure is introduced for region merging, offering accurate segmentation of objects and extending temporal consistency between the tracked partitions to hierarchical representations of every frame within the shot. Experimental results are presented, illustrating the usefulness of the approach.