Rate-distortion optimization (RDO) is widely applied in video coding, which aims at minimizing the coding distortion at a target bitrate. Conventionally, RDO is performed independently on each individual frame to avoid high computational complexity. However, extensive use of temporal/spatial predictions result in strong coding dependencies among neighboring frames, which make the current RDO be non-optimally used. To further improve video coding performance, it would be desirable to perform global RDO among a group of neighboring frames while maintaining approximately the same coding complexity. In this paper, the problem of global RDO is studied by jointly determining the quantization parameters (QPs) for a group of neighboring frames. Specifically, an adaptive frame-level QP selection algorithm is proposed for the H.265/HEVC random access coding by taking into account the inter-frame dependency. To measure the inter-frame dependency, a model based on the energy of prediction residuals is first established. With the help of the model, the problem of global RDO is then analyzed for the hierarchical coding structure in H.265/HEVC. Finally, the QP and the corresponding Lagrangian multiplier for each coding frame are determined adaptively by considering the total impact of its coding distortion on that of future frames in the encoding order. Experimental results show that in comparison with HM-16.0, the proposed algorithm reduces, on average, the BD-rate by 3.49% with negligible increase of encoding time. In addition, the quality fluctuation of the coded video by the proposed algorithm is lower than that by HM-16.0.

With the development of multimedia technologies and virtual reality display devices, omnidirectional videos have gained popularity nowadays. To reduce the bandwidth requirement for omnidirectional video transmission, tile-based viewport adaptive streaming methods have been proposed in the literatures. Challenges related to decoding the tiles simultaneously with limited number of decoders, and ensuring user's viewing experience during the viewport switch are still to be solved. In this paper, a two-layer fast viewport switching dynamic adaptive streaming over HTTP (DASH) system based on tiling mechanism is proposed, which incorporates the viewing trajectory of end users. To deal with the simultaneously decoding problem, an open group of picture (GOP) technique is proposed to enable merging different types of tiles into a composite stream at the client side. To reduce the quality recovery duration after the viewport change, a fast-switching strategy is also proposed. Moreover, considering the priorities of different types of chunks, a download strategy is further proposed to adapt the bandwidth fluctuations and viewport changes. Experimental results showed that the proposed system can significantly reduce the recovery duration of high quality video by approximately 90%, which can provide a better viewing experience to end users.

Due to the ability to depict large-scale 3D scenes, point clouds acquired by the Light Detection And Ranging (LiDAR) devices have played an indispensable role in various fields. The growing data amount of point cloud, however, brings huge challenges to existing point cloud processing networks. Developing point cloud compression algorithms has become an active research area in recent years. Representative compression frameworks include the MPEG Geometry-based Point Cloud Compression (G-PCC) standard in which a dedicated profile is designed for spinning LiDAR point clouds. In that design, prior knowledge of the LiDAR device is used to project points to nodes in a predictive structure which better reflects the spatial correlation of LiDAR point clouds. In this paper, an analysis has been conducted to explain the observed irregular point distribution in the predictive structure. A regularized projection algorithm is then proposed to construct a reliable prediction relationship in the predictive structure. Simplified geometry prediction techniques are further proposed based on the regularized projection pattern. Experimental results show that an average BD-rate gain of 18% can be achieved with lower encoding runtime if compared with MPEG G-PCC.

This paper investigates the problem of packet scheduling and resource allocation for multi-user wireless video transmission. The problem is first formulated as an optimization problem with the objective of minimizing the reconstructed video distortion of each user under the radio resource constraints. In order to solve the optimization problem, a crosslayer algorithm with low complexity is proposed by taking the radio resource constraints and the fairness across multi-user into account. Simulation results show that the proposed crosslayer algorithm outperforms the state-of-the-art packet scheduling and resource allocation algorithms by up to 1.2 dB in terms of the quality of the reconstructed video, and the fairness across different users is also maintained.

Visual content is increasingly being processed by machines for various automated content analysis tasks instead of being consumed by humans. Despite the existence of several compression methods tailored for machine tasks, few consider real-world scenarios with multiple tasks. In this paper, we aim to address this gap by proposing a task-switchable pre-processor that optimizes input images specifically for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. The proposed task-switchable pre-processor adeptly maintains relevant semantic information based on the specific characteristics of different downstream tasks, while effectively suppressing irrelevant information to reduce bitrate. To enhance the processing of semantic information for diverse tasks, we leverage pre-extracted semantic features to modulate the pixel-to-pixel mapping within the pre-processor. By switching between different modulations, multiple tasks can be seamlessly incorporated into the system. Extensive experiments demonstrate the practicality and simplicity of our approach. It significantly reduces the number of parameters required for handling multiple tasks while still delivering impressive performance. Our method showcases the potential to achieve efficient and effective compression for machine vision tasks, supporting the evolving demands of real-world applications.

The diverse display screen imposes significant challenges for assessing the perceptual media quality across different mobile devices. In this paper, the perceived image quality on different mobile phones is investigated. Firstly, subjective experiments for image quality evaluation are implemented on 9 popular mobile phones and a broadcast-quality monitor to evaluate the impact on perceived image quality regarding the screen resolution, screen size, image resolution, and image coding quality. Furthermore, the effect of mutual interaction between the image resolution and screen resolution is analyzed and an integrated assessment parameter is proposed to establish a device-dependent image quality assessment model. This model can be used to predict the user’s perceptual quality of the images displayed on different mobile devices. Experimental results using twofold cross-validation indicate that the proposed model can accurately estimate users’ perceived image quality on mobile devices.