A comparative study of scalable video coding schemes utilizing wavelet technology

Video transmission over variable-bandwidth networks requires instantaneous bit-rate adaptation at the server site to provide an acceptable decoding quality. For this purpose, recent developments in video coding aim at providing a fully embedded bit-stream with seamless adaptation capabilities in bit-rate, frame-rate and resolution. A new promising technology in this context is wavelet-based video coding. Wavelets have already demonstrated their potential for quality and resolution scalability in still-image coding. This led to the investigation of various schemes for the compression of video, exploiting similar principles to generate embedded bit-streams. In this paper we present scalable wavelet-based video-coding technology with competitive rate-distortion behavior compared to standardized non-scalable technology

Efficient depth-aware image deformation adaptation for curved screen displays

The curved screen has attracted considerable attentions in recent years, since it enables to enlarge the view angle and to enhance the immersive perception for users. However, existing curved surface projections are frequently prone to geometric distortion or loss of content. This paper presents a content-aware and depth-aware image adaptation solution for curved displays. An efficient optimization approach of image deformation is proposed to preserve local scene content and to minimize scene geometry distortion. To follow the original 3D perception of objects in different depth layers, the depth information is re-mapped for individual content scaling. Objective evaluation results reveal that our approach can effectively preserve foreground objects. We also perform a subjective evaluation of the proposed solution, and compare it to two alternative mapping methods, which are tested on different curvatures on both a traditional screen and an ad-hoc curvature-controllable curved display. Experimental results demonstrate that our approach outperforms other existing mapping methods for immersive display of rectangle images on curved screens

Sargassum detection and path estimation using neural networks

Sargassum has affected the Mexican Caribbean coasts since 2015 in atypical amounts, causing economic and ecological problems. Removal once it reaches the coast is complex since it is not easily separated from the sand, damaging dune vegetation, heavy transport compacts the sand and further deteriorates the coastline. Therefore, it is important to detect and estimate the sargassum mats path to optimize the collection efforts in the water. There have been some improvements in systems that rely on satellite images to determine areas and possible paths of sargassum, but these methods do not solve the problems near the coastline where the big mats observed in deep sea end up segregating in little mats which often do not show up in the satellite images. Besides, the temporal scales of nearshore sargassum dynamics are characterized by finer temporal resolution. This paper focuses on cameras located near the coast of Puerto Morelos reef lagoon where images are recorded of both beach and near-coastal sea. First, we apply preprocessing techniques based on time that allows us to discriminate the moving sargassum mats from the static sea bottom, then, using classic image processing techniques and neural networks we detect, trace, and estimate the path of the mat towards the place of arrival on the beach. We compared classic algorithms with neural networks. Some of the algorithms we tested are k-means and random forest for segmentation and dense optical flow to follow and estimate the path. This new methodology allows to supervise in real time the demeanor of sargassum close to shore without complex technical support

Supporting Wider Baseline Light Fields in JPEG Pleno With a Novel Slanted 4D-DCT Coding Mode

Light fields are one of the emerging 3D representation formats with an effective potential to offer very realistic and immersive visual experiences. This capability comes at the cost of a very large amount of acquired data which practical use requires efficient coding solutions. This need was already addressed by the JPEG Pleno Light Field Coding standard for static light fields, which has specified two coding modes, named 4D-Transform and 4D-Prediction. While the first offers better compression performance for smaller baseline light fields, the second excels for larger baseline light fields. This paper intends to propose a novel light field coding mode, the Slanted 4D-Transform coding mode, which extends the 4D-Transform coding mode based on the conventional 4D-DCT, to offer better compression performance than both the available JPEG Pleno coding modes, independently of the baseline. The key idea is to apply first to each 4D block in the light field an adaptive, hierarchical geometric transformation, which makes the data in the block more energy-compaction friendly for the following 4D-DCT. The rate-distortion performance results show that the proposed Slanted 4D-Transform codec is able to outperform both the already standardized JPEG Pleno coding modes, showing BD-Rates gains of 31.03% and 28.30% for the 4D-Transform and 4D-Prediction modes, respectively, thus implying that a single coding mode can efficiently code all types of light fields