The illumination of images can significantly impact computer-vision applications such as image classification, multiple object detection, and tracking, leading to a significant decline in detection and tracking accuracy. Recent advancements in deep learning techniques have been applied to Low-Light Image Enhancement (LLIE) to combat this issue. Retinex theory-based methods following a decomposition-adjustment pipeline for LLIE have performed well in various aspects. Despite their success, current research on Retinex-based deep learning still needs to improve in terms of optimization techniques and complicated convolution connections, which can be computationally intensive for end-device deployment. We propose an Optimized Retinex-Based CNN (OptiRet-Net) deep learning framework to address these challenges for the LLIE problem. Our results demonstrate that the proposed method outperforms existing state-of-the-art models in terms of full reference metrics with a PSNR of 21.87, SSIM of 0.80, LPIPS of 0.16, and zero reference metrics with a NIQE of 3.4 and PIQE of 56.6. Additionally, we validate our approach using a comprehensive evaluation comprising five datasets and nine prior methods. Furthermore, we assess the efficacy of our proposed model combining low-light multiple object tracking applications using YOLOX and ByteTrack in Versatile Video Coding (VVC/H.266) across various quantization parameters. Our findings reveal that LLIE-enhanced frames surpass their tracking results with a MOTA of 80.6% and a remarkable precision rate of 96%. Our model also achieves minimal file sizes by effectively compressing the enhanced low-light images while maintaining their quality, making it suitable for resource-constrained environments where storage or bandwidth limitations are a concern.

More users have a growing interest in low latency over-the-top (OTT) applications such as online video gaming, video chat, online casino, sports betting, and live auctions. OTT applications face challenges in delivering low latency live streams using Dynamic Adaptive Streaming over HTTP (DASH) due to large playback buffer and video segment duration. A potential solution to this issue is the use of HTTP chunked transfer encoding (CTE) with the common media application format (CMAF). This combination allows the delivery of each segment in several chunks to the client, starting before the segment is fully available in real-time. However, CTE and CMAF alone are not sufficient as they do not address other limitations and challenges at the client-side, including inaccurate bandwidth measurement, latency control, and bitrate selection.

360-degree video streaming commonly encodes and transmits the video as independently-decodable tiles to conserve bandwidth of regions out of the viewer's field of view (FoV). The bitrate of the tiles, however, can vary significantly across the tiles, complicating the choice of the representation to download for each tile in each segment to adapt to the bandwidth dynamics. In this paper, we model the tile rate allocation problem as a multiclass knapsack problem with a dynamic profit function that is a function of the FoV and the buffer occupancy. Experiments show that our approach can reduce bandwidth wastage by up to 41%, the number of stalls by up to 31%, stall durations by up to 26.5%, switches in quality by up to 20%, without sacrificing the quality of the tiles within the FoV, even when there are significant head movement and changes in FoV during streaming.

The significant popularity of HTTP adaptive video streaming (HAS), such as Dynamic Adaptive Streaming over HTTP (DASH), over the Internet has led to a stark increase in user expectations in terms of video quality and delivery robustness. This situation creates new challenges for content providers who must satisfy the Quality-of-Experience (QoE) requirements and demands of their customers over a best-effort network infrastructure. Unlike traditional single server DASH, we developed a D istributed Q ueuing theory bitrate adaptation algorithm for DASH (DQ-DASH) that leverages the availability of multiple servers by downloading segments in parallel. DQ-DASH uses a M x /D/1/K queuing theory based bitrate selection in conjunction with the request scheduler to download subsequent segments of the same quality through parallel requests to reduce quality fluctuations. DQ-DASH facilitates the aggregation of bandwidth from different servers and increases fault-tolerance and robustness through path diversity. The resulting resilience prevents clients from suffering QoE degradations when some of the servers become congested. DQ-DASH also helps to fully utilize the aggregate bandwidth from the servers and download the imminently required segment from the server with the highest throughput. We have also analyzed the effect of buffer capacity and segment duration for multi-source video streaming.

We demonstrate the use of DASH, a widely-deployed standard for streaming video content, for streaming 3D content in an NVE (Networked Virtual Environment) consisting of 3D geometry and associated textures. We have developed a DASH client for NVE to show how NVE benefits from the advantages of DASH: it offers a scalable, easy-to-deploy 3D streaming framework. In our system, the 3D content is first statically partitioned into compliant DASH data, and metadata is provided in order for the client to manage which data to download. Based on a proposed utility metric for geometry and texture at the different resolution, the client can choose the content to request depending on its viewpoint. We effectively provide a Web-based client to navigate through our sample 3D scene, while deriving the streaming requests from its computation of the necessary online parameters, in a receiver-driven manner.

DASH is now a widely deployed standard for streaming video content due to its simplicity, scalability, and ease of deployment. In this paper, we explore the use of DASH for a different type of media content -- networked virtual environment (NVE), with different properties and requirements. We organize a polygon soup with textures into a structure that is compatible with DASH MPD (Media Presentation Description), with a minimal set of view-independent metadata for the client to make intelligent decisions about what data to download at which resolution. We also present a DASH-based NVE client that uses a view-dependent and network dependent utility metric to decide what to download, based only on the information in the MPD file. We show that DASH can be used on NVE for 3D content streaming. Our work opens up the possibility of using DASH for highly interactive applications, beyond its current use in video streaming.

DASH, or Dynamic Adaptive Streaming over HTTP, relies on a rate adaptation component to decide on which representation to download for each video segment. A plethora of rate adaptation algorithms has been proposed in recent years. The decisions of which bitrate to download made by these algorithms largely depend on several factors: estimated network throughput, buffer occupancy, and buffer capacity. Yet, these algorithms are not informed by a fundamental relationship between these factors and the chosen bitrate, and as a result, we found that they do not perform consistently in all scenarios, and require parameter tuning to work well under different buffer capacity. In this paper, we model a DASH client as an M/D/1/K queue, which allows us to calculate the expected buffer occupancy given a bitrate choice, network throughput, and buffer capacity. Using this model, we propose QUETRA, a simple rate adaptation algorithm. We evaluated QUETRA under a diverse set of scenarios and found that, despite its simplicity, it leads to better quality of experience (7% - 140%) than existing algorithms.