Video streaming services over vehicular ad-hoc networks (VANETs) are in high demand for numerous applications associated with the connected vehicle (infotainment, driver assistance, accident support, etc.). However, streaming high-quality video through a VANET is not a trivial task, as the wireless channel is highly unreliable and suffers from bandwidth constraints. As a consequence, many packets may be lost, making it very difficult for the receiver to reconstruct a video with the minimum quality required. Our proposed scheme will combine several aspects of the overall video streaming architecture by following a cross-layer approach that includes: (a) the video packet stream content characteristics, (b) an adaptive forward error correction coding scheme, and (c) the use of QoS services. An adaptive RaptorQ coding scheme is proposed to protect the video packet stream without wasting the available network bandwidth. At the same time, we will use the QoS differentiated services of IEEE 802.11p to prioritise critical video packets, in order to avoid degradation of video quality during streaming. Finally, we will provide a mechanism to reduce the impact of synchronisation effects on the IEEE 1609.4 multiplexed service channel, which will reduce the packet collisions at the beginning of the service channel slot. All of these techniques, when properly combined, will enable high-quality video streaming services in urban VANET scenarios, thus providing a pleasant video quality experience to users even under different network conditions, with moderate to high packet error rates. In order to test the performance of our proposal, we will use a highly detailed simulation framework under different network conditions. The results of this work are expected to provide a feasible solution for high-quality video streaming services in urban VANETs.

Abstract This paper introduces a dual hybrid neural network model combining convolutional neural networks (CNNs) and artificial neural networks (ANNs) to optimize the quantization parameter (QP) for both $$64\times 64$$ 64 × 64 and $$32\times 32$$ 32 × 32 blocks in the versatile video coding (VVC) standard, enhancing video quality and compression efficiency. The model employs CNNs for spatial feature extraction and ANNs for structured data handling, addressing the limitations of current heuristic and just noticeable distortion (JND)-based methods. A dataset of luminance channel image blocks, encoded with various QP values, is generated and preprocessed, and the dual hybrid network structure is designed with convolutional and dense layers. The QP optimization is applied at two levels: the $$64\times 64$$ 64 × 64 model provides a global QP offset, while the $$32\times 32$$ 32 × 32 model refines the QP for further partitioned blocks. Performance evaluations using model error metrics like mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), as well as perceptual metrics like weighted PSNR (WPSNR), MS-SSIM, PSNR-HVS-M, and VMAF, demonstrate the model’s effectiveness. While our approach performs competitively with state-of-the-art algorithms, it significantly outperforms in VMAF, the most advanced and widely adopted perceptual quality metric. Furthermore, the dual-model approach yields better results at lower resolutions, whereas the single-model approach is more effective at higher resolutions. These results highlight the adaptability of the proposed models, offering improvements in both compression efficiency and perceptual quality, making them highly suitable for practical applications in modern video coding.

Abstract It is well-known that each new video coding standard significantly increases in computational complexity with respect to previous standards, and this is particularly true for the HEVC and VVC video coding standards. The development of techniques for reducing the required complexity without affecting the rate/distortion (R/D) performance is therefore always a topic of intense research interest. In this paper, we propose a combination of two powerful techniques, deep learning and parallel computing, to significantly reduce the complexity of the HEVC encoding engine. Our experimental results show that a combination of deep learning to reduce the CTU partitioning complexity with parallel strategies based on frame partitioning is able to achieve speedups of up to 26 $$\times$$ × when 16 threads are used. The R/D penalty in terms of the BD-BR metric depends on the video content, the compression rate and the number of OpenMP threads, and was consistently between 0.35 and 10% for the video sequence test set used in our experiments

In a near future, video transmission capabilities in intelligent vehicular networks will be essential for deploying high-demanded multimedia services for drivers and passengers. Applications and services like video on demand, iTV, context-aware video commercials, touristic information, driving assistance, multimedia e-call, etc., will be part of the common multimedia service-set of future transportation systems. However, wireless vehicular networks introduce several constraints that may seriously impact on the final quality of the video content delivery process. Factors like the shared-medium communication model, the limited bandwidth, the unconstrained delays, the signal propagation issues, and the node mobility, will be the ones that will degrade video delivery performance, so it will be a hard task to guarantee the minimum quality of service required by video applications. In this work, we will study how these factors impact on the received video quality by using a detailed simulation model of a urban vehicular network scenario. We will apply different techniques to reduce the video quality degradation produced by the transmission impairments like (a) Intra-refresh video coding modes, (b) frame partitioning (tiles/slices), and (c) quality of service at the Medium Access Control (MAC) level. So, we will learn how these techniques are able to fight against the network impairments produced by the hostile environment typically found in vehicular network scenarios. The experiments were carried out with a simulation environment based on the OMNeT++, Veins and SUMO simulators. Results show that the combination of the proposed techniques significantly improves the robustness of video transmission in vehicular networks, paving the way, with a wise collaboration with other techniques, to achieve a robust video delivery system that supports multimedia applications in future intelligent transportation systems.

Vehicular Ad-hoc Networks contribute to the Intelligent Transportation Systems by providing a set of services related to traffic, mobility, safe driving, and infotainment applications. One of the most challenging applications is video delivery, since it has to deal with several hurdles typically found in wireless communications, like high node mobility, bandwidth limitations and high loss rates. In this work, we propose an integrated simulation framework that will provide a multilayer view of a particular video delivery session with a bunch of simulation results at physical (i.e., collisions), MAC (i.e., packet delay), application (i.e., % of lost frames), and user levels (i.e., perceptual video quality). With this tool, we can analyze the performance of video streaming over vehicular networks with a high level of detail, giving us the keys to better understand and, as a consequence, improve video delivery services.

Uniform quantization schemas with dead zone are commonly used in image and video codecs. The design of these quantizers affects to the final R/D performance, being two of the quantizer parameters, the responsible for that variations: (a) the dead zone size and (b) the reconstruction point location inside each quantization step. We analyze how variations of these parameters, by means of a variable dead zone quantizer, affect to the R/D performance of wavelet-based image encoders, using three different quality metrics. We tune the quantizer for each image to obtain the optimum parameters that provide the best R/D behavior for each of the metrics for different rate ranges, without altering the rest of the encoder stages. We provide a general parameter set for each metric and rate range, to be used with other images to obtain important rate savings and better quality values for each metric.

Depth Image Base Rendering (DIBR) is an effective approach for 3D-TV, however quality and time consistence of the depth map is a problem in this field. Our intermediate solution between Intra and Inter encoders is able to cope with the quality and time consistency of the captured depth map info. Our encoder achieves the same visual quality than H264/AVC and x264 in Intra mode reducing coding delays.

This paper presents a perceptually enhanced intra-mode video encoder based on the Contrast Sensitivity Function (CSF) with a gracefully quality degradation as compression rate increases. The proposed encoder is highly competitive especially for high definition video formats at high video quality applications with constrained real-time and power processing demands.

Wavelet transforms have proved to be very powerful tools for image compression, since many state-of-the-art image codecs employ DWT into their algorithms. One advantage of this transform is the provision of both frequency and spatial localization of image energy compacted into a small fraction of the transform coefficients, equally likely to be positive or negative. Previous studies have verified that there is a strong correlation between the sign of a wavelet coefficient and the signs of their neighbors. This correlation opens the possibility of using a sign predictor in order to improve the image compression process. In this work we evaluate two algorithms, one based on Genetic programming and other based on Simulated Annealing process in order to obtain a good wavelet sign predictor.

The 3D-DWT is a mathematical tool of increasing importance in those applications that require an efficient processing of volumetric info. Other applications like professional video editing, IPTV video surveillance applications, live event IPTV broadcast, multi-spectral satellite imaging, HQ video delivery, etc, would rather use 3D-DWT encoders to reconstruct a frame as fast as possible. However, the huge memory requirement of the algorithms that compute the 3D-DWT is one of the main drawbacks in practical implementations. In this paper, we introduce a fast frame-based 3D-DWT video encoder with low memory usage. In addition, there is no need to divide the input video sequence into group of pictures (GOP), and it can be applied in a continuous manner, so that no boundary effects between GOPs appear.

Traditional embedded coding systems involve high complexity algorithms, requiring fast and expensive processors. In the last years, several authors have developed very fast and simple non-embedded wavelet encoders that are able to get reasonable good performance with reduced computing requirements. These encoders have lost the SNR scalability and precise rate control capabilities. In this paper, we propose a new non-embedded LTW codec version (E-LTW) with precise rate control method and good R/D performance due to the use of intra band neighboring context modeling for sign coding.

The 3D-DWT is a mathematical tool of increasing importance. However, the huge memory requirement of the algorithms that compute it is one of the main drawbacks in practical implementations. In this paper, we introduce a frame-by-frame algorithm to calculate the 3D-DWT with low memory usage. This algorithm is general, in the sense that it can be employed with any wavelet transform and, contrary to other proposals, it gets the same results as the regular wavelet transform. In addition, there is no need to divide the input video sequence into group of frames, and it can be applied in a continuous manner, so that coding efficiency is increased and no blocking artifacts appear.

Recently, there has been an increasing interest in the design of very fast wavelet image encoders focused on applications (interactive real-time image&video applications, GIS systems, etc) and devices (digital cameras, mobile phones, PDAs, etc) where coding delay and/or available computing resources (working memory and power processing) are critical for proper operation. Most of these fast wavelet image encoders are non-embedded in order to reduce complexity, so no rate control tools are available for scalable coding applications. In this work, we analyze the impact of simple rate control tools for these encoders in order to determine if the inclusion of rate control functionality is worth enough with respect to popular embedded encoders like SPIHT and JPEG2000. We perform the study by adding rate control to the nonembedded LTW encoder, showing that the increase in complexity still maintains LTW competitive with respect SPIHT and JPEG2000 in terms of R/D performance, coding delay and memory consumption.

When comparing the performance of different video coding approaches, improvements or new codec designs, one of the most important performance metrics is the rate/distortion (R/D), where distortion use to be measured in terms of PSNR (peak signal-to-noise ratio) values. However, it is well known that this metric not always capture the distortion perceived by the human being. So, a lot efforts were performed to define an objective video quality metric that is able to measure video quality distortion close to the one perceived for the destination user. In this work, we perform a study of different available objective quality metrics in order to evaluate their behaviour, taking as reference the classical PSNR metric. Our purpose is to find, if any, a video quality metric that is able to substitute PSNR for video quality assessment and determine a more accurate R/D performance metric when designing and evaluating video codec proposals