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We introduce FIDAVL: Fake Image Detection and Attribution using a Vision-Language Model. FIDAVL is a novel and efficient mul-titask approach inspired by the synergies between vision and language processing. Leveraging the benefits of zero-shot learning, FIDAVL exploits the complementarity between vision and language along with soft prompt-tuning strategy to detect fake images and accurately attribute them to their originating source models. We conducted extensive experiments on a comprehensive dataset comprising synthetic images generated by various state-of-the-art models. Our results demonstrate that FIDAVL achieves an encouraging average detection accuracy of 95.42% and F1-score of 95.47% while also obtaining noteworthy performance metrics, with an average F1-score of 92.64% and ROUGE-L score of 96.50% for attributing synthetic images to their respective source generation models. The source code of this work will be publicly released at https://github.com/Mamadou-Keita/FIDAVL.

In recent years, the emergence of models capable of generating images from text has attracted considerable interest, offering the possibility of creating realistic images from text descriptions. Yet these advances have also raised concerns about the potential misuse of these images, including the creation of misleading content such as fake news and propaganda. This study investigates the effectiveness of using advanced vision-language models (VLMs) for synthetic image identification. Specifically, the focus is on tuning state-of-the-art image captioning models for synthetic image detection. By harnessing the robust understanding capabilities of large VLMs, the aim is to distinguish authentic images from synthetic images produced by diffusion-based models. This study contributes to the advancement of synthetic image detection by exploiting the capabilities of visual language models such as BLIP-2 and ViTGPT2. By tailoring image captioning models, we address the challenges associated with the potential misuse of synthetic images in real-world applications. Results described in this paper highlight the promising role of VLMs in the field of synthetic image detection, outperforming conventional image-based detection techniques. Code and models can be found at https://github.com/Mamadou-Keita/VLM-DETECT., Comment: arXiv admin note: substantial text overlap with arXiv:2404.01959

Advancements in deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs), have ushered in an era of generating highly realistic images. While this technological progress has captured significant interest, it has also raised concerns about the potential difficulty in distinguishing real images from their synthetic counterparts. This paper takes inspiration from the potent convergence capabilities between vision and language, coupled with the zero-shot nature of vision-language models (VLMs). We introduce an innovative method called Bi-LORA that leverages VLMs, combined with low-rank adaptation (LORA) tuning techniques, to enhance the precision of synthetic image detection for unseen model-generated images. The pivotal conceptual shift in our methodology revolves around reframing binary classification as an image captioning task, leveraging the distinctive capabilities of cutting-edge VLM, notably bootstrapping language image pre-training (BLIP2). Rigorous and comprehensive experiments are conducted to validate the effectiveness of our proposed approach, particularly in detecting unseen diffusion-generated images from unknown diffusion-based generative models during training, showcasing robustness to noise, and demonstrating generalization capabilities to GANs. The obtained results showcase an impressive average accuracy of 93.41% in synthetic image detection on unseen generation models. The code and models associated with this research can be publicly accessed at https://github.com/Mamadou-Keita/VLM-DETECT.

Over the past two decades, the Internet-of-Things (IoT) has become a transformative concept, and as we approach 2030, a new paradigm known as the Internet of Senses (IoS) is emerging. Unlike conventional Virtual Reality (VR), IoS seeks to provide multi-sensory experiences, acknowledging that in our physical reality, our perception extends far beyond just sight and sound; it encompasses a range of senses. This article explores the existing technologies driving immersive multi-sensory media, delving into their capabilities and potential applications. This exploration includes a comparative analysis between conventional immersive media streaming and a proposed use case that leverages semantic communication empowered by generative Artificial Intelligence (AI). The focal point of this analysis is the substantial reduction in bandwidth consumption by 99.93% in the proposed scheme. Through this comparison, we aim to underscore the practical applications of generative AI for immersive media. Concurrently addressing major challenges in this field, such as temporal synchronization of multiple media, ensuring high throughput, minimizing the End-to-End (E2E) latency, and robustness to low bandwidth while outlining future trajectories.

Omnidirectional or 360-degree video is being increasingly deployed, largely due to the latest advancements in immersive virtual reality (VR) and extended reality (XR) technology. However, the adoption of these videos in streaming encounters challenges related to bandwidth and latency, particularly in mobility conditions such as with unmanned aerial vehicles (UAVs). Adaptive resolution and compression aim to preserve quality while maintaining low latency under these constraints, yet downscaling and encoding can still degrade quality and introduce artifacts. Machine learning (ML)-based super-resolution (SR) and quality enhancement techniques offer a promising solution by enhancing detail recovery and reducing compression artifacts. However, current publicly available 360-degree video SR datasets lack compression artifacts, which limit research in this field. To bridge this gap, this paper introduces omnidirectional video streaming dataset (ODVista), which comprises 200 high-resolution and high quality videos downscaled and encoded at four bitrate ranges using the high-efficiency video coding (HEVC)/H.265 standard. Evaluations show that the dataset not only features a wide variety of scenes but also spans different levels of content complexity, which is crucial for robust solutions that perform well in real-world scenarios and generalize across diverse visual environments. Additionally, we evaluate the performance, considering both quality enhancement and runtime, of two handcrafted and two ML-based SR models on the validation and testing sets of ODVista.

Neural fields, also known as implicit neural representations (INRs), have shown a remarkable capability of representing, generating, and manipulating various data types, allowing for continuous data reconstruction at a low memory footprint. Though promising, INRs applied to video compression still need to improve their rate-distortion performance by a large margin, and require a huge number of parameters and long training iterations to capture high-frequency details, limiting their wider applicability. Resolving this problem remains a quite challenging task, which would make INRs more accessible in compression tasks. We take a step towards resolving these shortcomings by introducing neural representations for videos NeRV++, an enhanced implicit neural video representation, as more straightforward yet effective enhancement over the original NeRV decoder architecture, featuring separable conv2d residual blocks (SCRBs) that sandwiches the upsampling block (UB), and a bilinear interpolation skip layer for improved feature representation. NeRV++ allows videos to be directly represented as a function approximated by a neural network, and significantly enhance the representation capacity beyond current INR-based video codecs. We evaluate our method on UVG, MCL JVC, and Bunny datasets, achieving competitive results for video compression with INRs. This achievement narrows the gap to autoencoder-based video coding, marking a significant stride in INR-based video compression research.

Over the past decade, the utilization of UAVs has witnessed significant growth, owing to their agility, rapid deployment, and maneuverability. In particular, the use of UAV-mounted 360-degree cameras to capture omnidirectional videos has enabled truly immersive viewing experiences with up to 6DoF. However, achieving this immersive experience necessitates encoding omnidirectional videos in high resolution, leading to increased bitrates. Consequently, new challenges arise in terms of latency, throughput, perceived quality, and energy consumption for real-time streaming of such content. This paper presents a comprehensive survey of research efforts in UAV-based immersive video streaming, benchmarks popular video encoding schemes, and identifies open research challenges. Initially, we review the literature on 360-degree video coding, packaging, and streaming, with a particular focus on standardization efforts to ensure interoperability of immersive video streaming devices and services. Subsequently, we provide a comprehensive review of research efforts focused on optimizing video streaming for timevarying UAV wireless channels. Additionally, we introduce a high resolution 360-degree video dataset captured from UAVs under different flying conditions. This dataset facilitates the evaluation of complexity and coding efficiency of software and hardware video encoders based on popular video coding standards and formats, including AVC/H.264, HEVC/H.265, VVC/H.266, VP9, and AV1. Our results demonstrate that HEVC achieves the best trade-off between coding efficiency and complexity through its hardware implementation, while AV1 format excels in coding efficiency through its software implementation, specifically using the libsvt-av1 encoder. Furthermore, we present a real testbed showcasing 360-degree video streaming over a UAV, enabling remote control of the drone via a 5G cellular network.

HTTP adaptive streaming (HAS) has emerged as a widely adopted approach for over-the-top (OTT) video streaming services, due to its ability to deliver a seamless streaming experience. A key component of HAS is the bitrate ladder, which provides the encoding parameters (e.g., bitrate-resolution pairs) to encode the source video. The representations in the bitrate ladder allow the client's player to dynamically adjust the quality of the video stream based on network conditions by selecting the most appropriate representation from the bitrate ladder. The most straightforward and lowest complexity approach involves using a fixed bitrate ladder for all videos, consisting of pre-determined bitrate-resolution pairs known as one-size-fits-all. Conversely, the most reliable technique relies on intensively encoding all resolutions over a wide range of bitrates to build the convex hull, thereby optimizing the bitrate ladder for each specific video. Several techniques have been proposed to predict content-based ladders without performing a costly exhaustive search encoding. This paper provides a comprehensive review of various methods, including both conventional and learning-based approaches. Furthermore, we conduct a benchmark study focusing exclusively on various learning-based approaches for predicting content-optimized bitrate ladders across multiple codec settings. The considered methods are evaluated on our proposed large-scale dataset, which includes 300 UHD video shots encoded with software and hardware encoders using three state-of-the-art encoders, including AVC/H.264, HEVC/H.265, and VVC/H.266, at various bitrate points. Our analysis provides baseline methods and insights, which will be valuable for future research in the field of bitrate ladder prediction. The source code of the proposed benchmark and the dataset will be made publicly available upon acceptance of the paper.

In recent years, the proliferation of multimedia applications and formats, such as IPTV, Virtual Reality (VR, 360-degree), and point cloud videos, has presented new challenges to the video compression research community. Simultaneously, there has been a growing demand from users for higher resolutions and improved visual quality. To further enhance coding efficiency, a new video coding standard, Versatile Video Coding (VVC), was introduced in July 2020. This paper conducts a comprehensive analysis of coding performance and complexity for the latest VVC standard in comparison to its predecessor, High Efficiency Video Coding (HEVC). The study employs a diverse set of test sequences, covering both High Definition (HD) and Ultra High Definition (UHD) resolutions, and spans a wide range of bit-rates. These sequences are encoded using the reference software encoders of HEVC (HM) and VVC (VTM). The results consistently demonstrate that VVC outperforms HEVC, achieving bit-rate savings of up to 40% on the subjective quality scale, particularly at realistic bit-rates and quality levels. Objective quality metrics, including PSNR, SSIM, and VMAF, support these findings, revealing bit-rate savings ranging from 31% to 40%, depending on the video content, spatial resolution, and the selected quality metric. However, these improvements in coding efficiency come at the cost of significantly increased computational complexity. On average, our results indicate that the VVC decoding process is 1.5 times more complex, while the encoding process becomes at least eight times more complex than that of the HEVC reference encoder. Our simultaneous profiling of the two standards sheds light on the primary evolutionary differences between them and highlights the specific stages responsible for the observed increase in complexity.

The high efficiency video coding (HEVC) standard and the joint exploration model (JEM) codec incorporate 35 and 67 intra prediction modes (IPMs) respectively, which are essential for efficient compression of Intra coded blocks. These IPMs are transmitted to the decoder through a coding scheme. In our paper, we present an innovative approach to construct a dedicated coding scheme for IPM based on contextual information. This approach comprises three key steps: prediction, clustering, and coding, each of which has been enhanced by introducing new elements, namely, labels for prediction, tests for clustering, and codes for coding. In this context, we have proposed a method that utilizes a genetic algorithm to minimize the rate cost, aiming to derive the most efficient coding scheme while leveraging the available labels, tests, and codes. The resulting coding scheme, expressed as a binary tree, achieves the highest coding efficiency for a given level of complexity. In our experimental evaluation under the HEVC standard, we observed significant bitrate gains while maintaining coding efficiency under the JEM codec. These results demonstrate the potential of our approach to improve compression efficiency, particularly under the HEVC standard, while preserving the coding efficiency of the JEM codec.

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance.

Over the last few years, neural image compression has gained wide attention from research and industry, yielding promising end-to-end deep neural codecs outperforming their conventional counterparts in rate-distortion performance. Despite significant advancement, current methods, including attention-based transform coding, still need to be improved in reducing the coding rate while preserving the reconstruction fidelity, especially in non-homogeneous textured image areas. Those models also require more parameters and a higher decoding time. To tackle the above challenges, we propose ConvNeXt-ChARM, an efficient ConvNeXt-based transform coding framework, paired with a compute-efficient channel-wise auto-regressive prior to capturing both global and local contexts from the hyper and quantized latent representations. The proposed architecture can be optimized end-to-end to fully exploit the context information and extract compact latent representation while reconstructing higher-quality images. Experimental results on four widely-used datasets showed that ConvNeXt-ChARM brings consistent and significant BD-rate (PSNR) reductions estimated on average to 5.24% and 1.22% over the versatile video coding (VVC) reference encoder (VTM-18.0) and the state-of-the-art learned image compression method SwinT-ChARM, respectively. Moreover, we provide model scaling studies to verify the computational efficiency of our approach and conduct several objective and subjective analyses to bring to the fore the performance gap between the next generation ConvNet, namely ConvNeXt, and Swin Transformer., Comment: arXiv admin note: substantial text overlap with arXiv:2307.02273. text overlap with arXiv:2307.06091

Motivated by the efficiency investigation of the Tranformer-based transform coding framework, namely SwinT-ChARM, we propose to enhance the latter, as first, with a more straightforward yet effective Tranformer-based channel-wise auto-regressive prior model, resulting in an absolute image compression transformer (ICT). Current methods that still rely on ConvNet-based entropy coding are limited in long-range modeling dependencies due to their local connectivity and an increasing number of architectural biases and priors. On the contrary, the proposed ICT can capture both global and local contexts from the latent representations and better parameterize the distribution of the quantized latents. Further, we leverage a learnable scaling module with a sandwich ConvNeXt-based pre/post-processor to accurately extract more compact latent representation while reconstructing higher-quality images. Extensive experimental results on benchmark datasets showed that the proposed adaptive image compression transformer (AICT) framework significantly improves the trade-off between coding efficiency and decoder complexity over the versatile video coding (VVC) reference encoder (VTM-18.0) and the neural codec SwinT-ChARM., Comment: arXiv admin note: substantial text overlap with arXiv:2307.02273

Recently, the performance of neural image compression (NIC) has steadily improved thanks to the last line of study, reaching or outperforming state-of-the-art conventional codecs. Despite significant progress, current NIC methods still rely on ConvNet-based entropy coding, limited in modeling long-range dependencies due to their local connectivity and the increasing number of architectural biases and priors, resulting in complex underperforming models with high decoding latency. Motivated by the efficiency investigation of the Tranformer-based transform coding framework, namely SwinT-ChARM, we propose to enhance the latter, as first, with a more straightforward yet effective Tranformer-based channel-wise auto-regressive prior model, resulting in an absolute image compression transformer (ICT). Through the proposed ICT, we can capture both global and local contexts from the latent representations and better parameterize the distribution of the quantized latents. Further, we leverage a learnable scaling module with a sandwich ConvNeXt-based pre-/post-processor to accurately extract more compact latent codes while reconstructing higher-quality images. Extensive experimental results on benchmark datasets showed that the proposed framework significantly improves the trade-off between coding efficiency and decoder complexity over the versatile video coding (VVC) reference encoder (VTM-18.0) and the neural codec SwinT-ChARM. Moreover, we provide model scaling studies to verify the computational efficiency of our approach and conduct several objective and subjective analyses to bring to the fore the performance gap between the adaptive image compression transformer (AICT) and the neural codec SwinT-ChARM.

The advent of the Metaverse concept has further expedited the evolution of haptic, tactile internet, and multimedia applications with their VR/AR/XR services, and therefore, fully-immersive sensing is most likely to define the next generation of wireless networks as a key to realize the speculative vision of the Metaverse. In this magazine, we articulate different types of media that we envision will be communicated between the cyber and physical twins in the Metaverse. In particular, we explore the advantages grasped by exploiting each kind, and we point out critical challenges pertinent to 3D data processing, coding, transporting, and rendering. We further shed light on the role of future wireless networks in delivering the anticipated quality of immersion through the reliable streaming of multimedia signals between the digital twin and its physical counterpart. Specifically, we explore emergent communication paradigms, including semantic, holographic, and goal-oriented communication, which we expect to realize energy and spectrally efficient Metaverse while ensuring ultra-low latency.

Thanks to the remarkable advances in generative adversarial networks (GANs), it is becoming increasingly easy to generate/manipulate images. The existing works have mainly focused on deepfake in face images and videos. However, we are currently witnessing the emergence of fake satellite images, which can be misleading or even threatening to national security. Consequently, there is an urgent need to develop detection methods capable of distinguishing between real and fake satellite images. To advance the field, in this paper, we explore the suitability of several convolutional neural network (CNN) architectures for fake satellite image detection. Specifically, we benchmark four CNN models by conducting extensive experiments to evaluate their performance and robustness against various image distortions. This work allows the establishment of new baselines and may be useful for the development of CNN-based methods for fake satellite image detection., Comment: IEEE International Workshop on Multimedia Signal Processing (MMSP'2022)

Recently, with the growing popularity of mobile devices as well as video sharing platforms (e.g., YouTube, Facebook, TikTok, and Twitch), User-Generated Content (UGC) videos have become increasingly common and now account for a large portion of multimedia traffic on the internet. Unlike professionally generated videos produced by filmmakers and videographers, typically, UGC videos contain multiple authentic distortions, generally introduced during capture and processing by naive users. Quality prediction of UGC videos is of paramount importance to optimize and monitor their processing in hosting platforms, such as their coding, transcoding, and streaming. However, blind quality prediction of UGC is quite challenging because the degradations of UGC videos are unknown and very diverse, in addition to the unavailability of pristine reference. Therefore, in this paper, we propose an accurate and efficient Blind Video Quality Assessment (BVQA) model for UGC videos, which we name 2BiVQA for double Bi-LSTM Video Quality Assessment. 2BiVQA metric consists of three main blocks, including a pre-trained Convolutional Neural Network (CNN) to extract discriminative features from image patches, which are then fed into two Recurrent Neural Networks (RNNs) for spatial and temporal pooling. Specifically, we use two Bi-directional Long Short Term Memory (Bi-LSTM) networks, the first is used to capture short-range dependencies between image patches, while the second allows capturing longrange dependencies between frames to account for the temporal memory effect. Experimental results on recent large-scale UGC VQA datasets show that 2BiVQA achieves high performance at lower computational cost than most state-of-the-art VQA models. The source code of our 2BiVQA metric is made publicly available at: https://github.com/atelili/2BiVQA

Changing the encoding parameters, in particular the video resolution, is a common practice before transcoding. To this end, streaming and broadcast platforms benefit from so-called bitrate ladders to determine the optimal resolution for given bitrates. However, the task of determining the bitrate ladder can usually be challenging as, on one hand, so-called fit-for-all static ladders would waste bandwidth, and on the other hand, fully specialized ladders are often not affordable in terms of computational complexity. In this paper, we propose an ML-based scheme for predicting the bitrate ladder based on the content of the video. The baseline of our solution predicts the bitrate ladder using two constituent methods, which require no encoding passes. To further enhance the performance of the constituent methods, we integrate a conditional ensemble method to aggregate their decisions, with a negligibly limited number of encoding passes. The experiment, carried out on the optimized software encoder implementation of the VVC standard, called VVenC, shows significant performance improvement. When compared to static bitrate ladder, the proposed method can offer about 13% bitrate reduction in terms of BD-BR with a negligible additional computational overhead. Conversely, when compared to the fully specialized bitrate ladder method, the proposed method can offer about 86% to 92% complexity reduction, at cost the of only 0.8% to 0.9% coding efficiency drop in terms of BD-BR.

The quality of patient care associated with diagnostic radiology is proportionate to a physician workload. Segmentation is a fundamental limiting precursor to both diagnostic and therapeutic procedures. Advances in machine learning (ML) aim to increase diagnostic efficiency by replacing a single application with generalized algorithms. The goal of unsupervised anomaly detection (UAD) is to identify potential anomalous regions unseen during training, where convolutional neural network (CNN) based autoencoders (AEs) and variational autoencoders (VAEs) are considered a de facto approach for reconstruction based-anomaly segmentation. The restricted receptive field in CNNs limits the CNN to model the global context. Hence, if the anomalous regions cover large parts of the image, the CNN-based AEs are not capable of bringing a semantic understanding of the image. Meanwhile, vision transformers (ViTs) have emerged as a competitive alternative to CNNs. It relies on the self-attention mechanism that can relate image patches to each other. We investigate in this paper Transformer capabilities in building AEs for the reconstruction-based UAD task to reconstruct a coherent and more realistic image. We focus on anomaly segmentation for brain magnetic resonance imaging (MRI) and present five Transformer-based models while enabling segmentation performance comparable to or superior to state-of-the-art (SOTA) models. The source code is made publicly available on GitHub: https://github.com/ahmedgh970/Transformers_Unsupervised_Anomaly_Segmentation.git.

In recent years, the global demand for high-resolution videos and the emergence of new multimedia applications have created the need for a new video coding standard. Hence, in July 2020 the Versatile Video Coding (VVC) standard was released providing up to 50% bit-rate saving for the same video quality compared to its predecessor High Efficiency Video Coding (HEVC). However, this bit-rate saving comes at the cost of a high computational complexity, particularly for live applications and on resource-constraint embedded devices. This paper presents two optimized VVC software decoders, named OpenVVC and Versatile Video deCoder (VVdeC), designed for low resources platforms. They exploit optimization techniques such as data level parallelism using Single Instruction Multiple Data (SIMD) instructions and functional level parallelism using frame, tile and slice-based parallelisms. Furthermore, a comparison in terms of decoding run time, energy and memory consumption between the two decoders is presented while targeting two different resource-constraint embedded devices. The results showed that both decoders achieve real-time decoding of Full High definition (FHD) resolution over the first platform using 8 cores and High-definition (HD) real-time decoding for the second platform using only 4 cores with comparable results in terms of average consumed energy: around 26 J and 15 J for the 8 cores and 4 cores embedded platforms, respectively. Regarding the memory usage, OpenVVC showed better results with less average maximum memory consumed during run time compared to VVdeC.

In this paper, deep learning-based techniques for film grain removal and synthesis that can be applied in video coding are proposed. Film grain is inherent in analog film content because of the physical process of capturing images and video on film. It can also be present in digital content where it is purposely added to reflect the era of analog film and to evoke certain emotions in the viewer or enhance the perceived quality. In the context of video coding, the random nature of film grain makes it both difficult to preserve and very expensive to compress. To better preserve it while compressing the content efficiently, film grain is removed and modeled before video encoding and then restored after video decoding. In this paper, a film grain removal model based on an encoder-decoder architecture and a film grain synthesis model based on a \ac{cgan} are proposed. Both models are trained on a large dataset of pairs of clean (grain-free) and grainy images. Quantitative and qualitative evaluations of the developed solutions were conducted and showed that the proposed film grain removal model is effective in filtering film grain at different intensity levels using two configurations: 1) a non-blind configuration where the film grain level of the grainy input is known and provided as input, 2) a blind configuration where the film grain level is unknown. As for the film grain synthesis task, the experimental results show that the proposed model is able to reproduce realistic film grain with a controllable intensity level specified as input.

Federated learning (FL) has emerged to enable global model training over distributed clients' data while preserving its privacy. However, the global trained model is vulnerable to the evasion attacks especially, the adversarial examples (AEs), carefully crafted samples to yield false classification. Adversarial training (AT) is found to be the most promising approach against evasion attacks and it is widely studied for convolutional neural network (CNN). Recently, vision transformers have been found to be effective in many computer vision tasks. To the best of the authors' knowledge, there is no work that studied the feasibility of AT in a FL process for vision transformers. This paper investigates such feasibility with different federated model aggregation methods and different vision transformer models with different tokenization and classification head techniques. In order to improve the robust accuracy of the models with the not independent and identically distributed (Non-IID), we propose an extension to FedAvg aggregation method, called FedWAvg. By measuring the similarities between the last layer of the global model and the last layer of the client updates, FedWAvg calculates the weights to aggregate the local models updates. The experiments show that FedWAvg improves the robust accuracy when compared with other state-of-the-art aggregation methods.

In the recent years, users requirements for higher resolution, coupled with the apparition of new multimedia applications, have created the need for a new video coding standard. The new generation video coding standard, called Versatile Video Coding (VVC), has been developed by the Joint Video Experts Team, and offers coding capability beyond the previous generation High Efficiency Video Coding (HEVC) standard. Due to the incorporation of more advanced and complex tools, the decoding complexity of VVC standard compared to HEVC has approximately doubled. This complexity increase raises new research challenges to achieve live software decoding. In this context, we developed OpenVVC, an open-source software decoder that supports a broad range of VVC functionalities. This paper presents the OpenVVC software architecture, its parallelism strategy as well as a detailed set of experimental results. By combining extensive data level parallelism with frame level parallelism, OpenVVC achieves real-time decoding of UHD video content. Moreover, the memory required by OpenVVC is remarkably low, which presents a great advantage for its integration on embedded platforms with low memory resources. The code of the OpenVVC decoder is publicly available at https://github.com/OpenVVC/OpenVVC

In this paper, we present CAESR, an hybrid learning-based coding approach for spatial scalability based on the versatile video coding (VVC) standard. Our framework considers a low-resolution signal encoded with VVC intra-mode as a base-layer (BL), and a deep conditional autoencoder with hyperprior (AE-HP) as an enhancement-layer (EL) model. The EL encoder takes as inputs both the upscaled BL reconstruction and the original image. Our approach relies on conditional coding that learns the optimal mixture of the source and the upscaled BL image, enabling better performance than residual coding. On the decoder side, a super-resolution (SR) module is used to recover high-resolution details and invert the conditional coding process. Experimental results have shown that our solution is competitive with the VVC full-resolution intra coding while being scalable.

The upcoming video coding standard, Versatile Video Coding (VVC), has shown great improvement compared to its predecessor, High Efficiency Video Coding (HEVC), in terms of bitrate saving. Despite its substantial performance, compressed videos might still suffer from quality degradation at low bitrates due to coding artifacts such as blockiness, blurriness and ringing. In this work, we exploit Convolutional Neural Networks (CNN) to enhance quality of VVC coded frames after decoding in order to reduce low bitrate artifacts. The main contribution of this work is the use of coding information from the compressed bitstream. More precisely, the prediction information of intra frames is used for training the network in addition to the reconstruction information. The proposed method is applied on both luminance and chrominance components of intra coded frames of VVC. Experiments on VVC Test Model (VTM) show that, both in low and high bitrates, the use of coding information can improve the BD-rate performance by about 1% and 6% for luma and chroma components, respectively.

With the growing data consumption of emerging video applications and users requirement for higher resolutions, up to 8K, a huge effort has been made in video compression technologies. Recently, versatile video coding (VVC) has been standardized by the moving picture expert group (MPEG), providing a significant improvement in compression performance over its predecessor high efficiency video coding (HEVC). In this paper, we provide a comparative subjective quality evaluation between VVC and HEVC standards for 8K resolution videos. In addition, we evaluate the perceived quality improvement offered by 8K over UHD 4K resolution. The compression performance of both VVC and HEVC standards has been conducted in random access (RA) coding configuration, using their respective reference software, VVC test model (VTM-11) and HEVC test model (HM-16.20). Objective measurements, using PSNR, MS-SSIM and VMAF metrics have shown that the bitrate gains offered by VVC over HEVC for 8K video content are around 31%, 26% and 35%, respectively. Subjectively, VVC offers an average of 40% of bitrate reduction over HEVC for the same visual quality. A compression gain of 50% has been reached for some tested video sequences regarding a Student t-test analysis. In addition, for most tested scenes, a significant visual difference between uncompressed 4K and 8K has been noticed., Comment: Paper under review

Versatile Video Coding (VVC) is the next generation video coding standard finalized in July 2020. VVC introduces new coding tools enhancing the coding efficiency compared to its predecessor High Efficiency Video Coding (HEVC). These new tools have a significant impact on the VVC software decoder complexity estimated to 2 times HEVC decoder complexity. In particular, the transform module includes in VVC separable and non-separable transforms named Multiple Transform Selection (MTS) and Low Frequency Non-Separable Transform (LFNST) tools, respectively. In this paper, we present an area-efficient hardware architecture of the inverse transform module for a VVC decoder. The proposed design uses a total of 64 regular multipliers in a pipelined architecture targeting Application-Specific Integrated Circuit (ASIC) platforms. It consists in a multi-standard architecture that supports the transform modules of recent MPEG standards including Advanced Video Coding (AVC), HEVC and VVC. The architecture leverages all primary and secondary transforms optimisations including butterfly de-composition, coefficients zeroing and the inherent linear relationship between the transforms. The synthesized results show that the proposed method sustains a constant throughput of 1sample per cycle and a constant latency for all block sizes. The proposed hardware inverse transform module operates at 600MHz frequency enabling to decode in real-time 4K video at 30 frames per second in 4:2:2 chroma sub-sampling format. The proposed module has been integrated in an ASIC UHD decoder targeting energy-aware decoding of VVC videos on consumer devices.

Despite the enormous performance of deepneural networks (DNNs), recent studies have shown theirvulnerability to adversarial examples (AEs), i.e., care-fully perturbed inputs designed to fool the targetedDNN. Currently, the literature is rich with many ef-fective attacks to craft such AEs. Meanwhile, many de-fenses strategies have been developed to mitigate thisvulnerability. However, these latter showed their effec-tiveness against specific attacks and does not general-ize well to different attacks. In this paper, we proposea framework for defending DNN classifier against ad-versarial samples. The proposed method is based on atwo-stage framework involving a separate detector anda denoising block. The detector aims to detect AEs bycharacterizing them through the use of natural scenestatistic (NSS), where we demonstrate that these statis-tical features are altered by the presence of adversarialperturbations. The denoiser is based on block matching3D (BM3D) filter fed by an optimum threshold valueestimated by a convolutional neural network (CNN) toproject back the samples detected as AEs into theirdata manifold. We conducted a complete evaluation onthree standard datasets namely MNIST, CIFAR-10 andTiny-ImageNet. The experimental results show that theproposed defense method outperforms the state-of-the-art defense techniques by improving the robustnessagainst a set of attacks under black-box, gray-box and white-box settings. The source code is available at: https://github.com/kherchouche-anouar/2DAE

As various databases of facial expressions have been made accessible over the last few decades, the Facial Expression Recognition (FER) task has gotten a lot of interest. The multiple sources of the available databases raised several challenges for facial recognition task. These challenges are usually addressed by Convolution Neural Network (CNN) architectures. Different from CNN models, a Transformer model based on attention mechanism has been presented recently to address vision tasks. One of the major issue with Transformers is the need of a large data for training, while most FER databases are limited compared to other vision applications. Therefore, we propose in this paper to learn a vision Transformer jointly with a Squeeze and Excitation (SE) block for FER task. The proposed method is evaluated on different publicly available FER databases including CK+, JAFFE,RAF-DB and SFEW. Experiments demonstrate that our model outperforms state-of-the-art methods on CK+ and SFEW and achieves competitive results on JAFFE and RAF-DB.

The amount of video content and the number of applications based on multimedia information increase each day. The development of new video coding standards is a challenge to increase the compression rate and other important features with a reasonable increase in the computational load. Video Experts Team (JVET) of ITU-T and the JCT group within ISO/IEC have worked together to standardize the Versatile Video Coding, approved finally in July 2020 as ITU-T H.266 | MPEG-I - Part 3 (ISO/IEC 23090-3) standard. This paper overviews some interesting consumer electronic use cases, the compression tools described in the standard, the current available real time implementations and the first industrial trials done with this standard.

The major part of the vanilla vision transformer (ViT) is the attention block that brings the power of mimicking the global context of the input image. For better performance, ViT needs large-scale training data. To overcome this data hunger limitation, many ViT-based networks, or hybrid-ViT, have been proposed to include local context during the training. The robustness of ViTs and its variants against adversarial attacks has not been widely investigated in the literature like CNNs. This work studies the robustness of ViT variants 1) against different Lp-based adversarial attacks in comparison with CNNs, 2) under adversarial examples (AEs) after applying preprocessing defense methods and 3) under the adaptive attacks using expectation over transformation (EOT) framework. To that end, we run a set of experiments on 1000 images from ImageNet-1k and then provide an analysis that reveals that vanilla ViT or hybrid-ViT are more robust than CNNs. For instance, we found that 1) Vanilla ViTs or hybrid-ViTs are more robust than CNNs under Lp-based attacks and under adaptive attacks. 2) Unlike hybrid-ViTs, Vanilla ViTs are not responding to preprocessing defenses that mainly reduce the high frequency components. Furthermore, feature maps, attention maps, and Grad-CAM visualization jointly with image quality measures, and perturbations' energy spectrum are provided for an insight understanding of attention-based models.

Salient human detection (SHD) in dynamic 360{\deg} immersive videos is of great importance for various applications such as robotics, inter-human and human-object interaction in augmented reality. However, 360{\deg} video SHD has been seldom discussed in the computer vision community due to a lack of datasets with large-scale omnidirectional videos and rich annotations. To this end, we propose SHD360, the first 360{\deg} video SHD dataset which contains various real-life daily scenes. Since so far there is no method proposed for 360{\deg} image/video SHD, we systematically benchmark 11 representative state-of-the-art salient object detection (SOD) approaches on our SHD360, and explore key issues derived from extensive experimenting results. We hope our proposed dataset and benchmark could serve as a good starting point for advancing human-centric researches towards 360{\deg} panoramic data. The dataset is available at https://github.com/PanoAsh/SHD360., Comment: 21 pages, 13 figures, 5 tables; Project page: https://github.com/PanoAsh/SHD360; Technical report

This paper presents a framework for Convolutional Neural Network (CNN)-based quality enhancement task, by taking advantage of coding information in the compressed video signal. The motivation is that normative decisions made by the encoder can significantly impact the type and strength of artifacts in the decoded images. In this paper, the main focus has been put on decisions defining the prediction signal in intra and inter frames. This information has been used in the training phase as well as input to help the process of learning artifacts that are specific to each coding type. Furthermore, to retain a low memory requirement for the proposed method, one model is used for all Quantization Parameters (QPs) with a QP-map, which is also shared between luma and chroma components. In addition to the Post Processing (PP) approach, the In-Loop Filtering (ILF) codec integration has also been considered, where the characteristics of the Group of Pictures (GoP) are taken into account to boost the performance. The proposed CNN-based Quality Enhancement(QE) framework has been implemented on top of the VVC Test Model (VTM-10). Experiments show that the prediction-aware aspect of the proposed method improves the coding efficiency gain of the default CNN-based QE method by 1.52%, in terms of BD-BR, at the same network complexity compared to the default CNN-based QE filter.

Artifact removal and filtering methods are inevitable parts of video coding. On one hand, new codecs and compression standards come with advanced in-loop filters and on the other hand, displays are equipped with high capacity processing units for post-treatment of decoded videos. This paper proposes a Convolutional Neural Network (CNN)-based post-processing algorithm for intra and inter frames of Versatile Video Coding (VVC) coded streams. Depending on the frame type, this method benefits from normative prediction signal by feeding it as an additional input along with reconstructed signal and a Quantization Parameter (QP)-map to the CNN. Moreover, an optional Model Selection (MS) strategy is adopted to pick the best trained model among available ones at the encoder side and signal it to the decoder side. This MS strategy is applicable at both frame level and block level. The experiments under the Random Access (RA) configuration of the VVC Test Model (VTM-10.0) show that the proposed prediction-aware algorithm can bring an additional BD-BR gain of -1.3% compared to the method without the prediction information. Furthermore, the proposed MS scheme brings -0.5% more BD-BR gain on top of the prediction-aware method.

This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a new type of image processing technology, perceptual image processing algorithms based on Generative Adversarial Networks (GAN) have produced images with more realistic textures. These output images have completely different characteristics from traditional distortions, thus pose a new challenge for IQA methods to evaluate their visual quality. In comparison with previous IQA challenges, the training and testing datasets in this challenge include the outputs of perceptual image processing algorithms and the corresponding subjective scores. Thus they can be used to develop and evaluate IQA methods on GAN-based distortions. The challenge has 270 registered participants in total. In the final testing stage, 13 participating teams submitted their models and fact sheets. Almost all of them have achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.

In the past few years, numerous deep learning methods have been proposed to address the task of segmenting salient objects from RGB images. However, these approaches depending on single modality fail to achieve the state-of-the-art performance on widely used light field salient object detection (SOD) datasets, which collect large-scale natural images and provide multiple modalities such as multi-view, micro-lens images and depth maps. Most recently proposed light field SOD methods have acquired improving detecting accuracy, yet still predict rough objects' structures and perform slow inference speed. To this end, we propose CMA-Net, which consists of two novel cascaded mutual attention modules aiming at fusing the high level features from the modalities of all-in-focus and depth. Our proposed CMA-Net outperforms 30 SOD methods on two widely applied light field benchmark datasets. Besides, the proposed CMA-Net is able to inference at the speed of 53 fps. Extensive quantitative and qualitative experiments illustrate both the effectiveness and efficiency of our CMA-Net., Comment: 6 pages, 4 figures, 2 tables

Deep learning (DL) has shown great success in many human-related tasks, which has led to its adoption in many computer vision based applications, such as security surveillance systems, autonomous vehicles and healthcare. Such safety-critical applications have to draw their path to success deployment once they have the capability to overcome safety-critical challenges. Among these challenges are the defense against or/and the detection of the adversarial examples (AEs). Adversaries can carefully craft small, often imperceptible, noise called perturbations to be added to the clean image to generate the AE. The aim of AE is to fool the DL model which makes it a potential risk for DL applications. Many test-time evasion attacks and countermeasures,i.e., defense or detection methods, are proposed in the literature. Moreover, few reviews and surveys were published and theoretically showed the taxonomy of the threats and the countermeasure methods with little focus in AE detection methods. In this paper, we focus on image classification task and attempt to provide a survey for detection methods of test-time evasion attacks on neural network classifiers. A detailed discussion for such methods is provided with experimental results for eight state-of-the-art detectors under different scenarios on four datasets. We also provide potential challenges and future perspectives for this research direction., Comment: Accepted and published in Artificial Intelligence Review journal

We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms. Our SA-Net exploits the rich information of focal stacks via 3D convolutional neural networks, decodes the high-level features of multi-modal light field data with two cascaded synergistic attention modules, and predicts the saliency map using an effective feature fusion module in a progressive manner. Extensive experiments on three widely-used benchmark datasets show that our SA-Net outperforms 28 state-of-the-art models, sufficiently demonstrating its effectiveness and superiority. Our code is available at https://github.com/PanoAsh/SA-Net., Comment: 20 pages, 12 figures; Project Page https://github.com/PanoAsh/SA-Net ; Accepted to BMVC-21

This paper introduces a practical learned video codec. Conditional coding and quantization gain vectors are used to provide flexibility to a single encoder/decoder pair, which is able to compress video sequences at a variable bitrate. The flexibility is leveraged at test time by choosing the rate and GOP structure to optimize a rate-distortion cost. Using the CLIC21 video test conditions, the proposed approach shows performance on par with HEVC.

The latest video coding standard, called versatile video coding (VVC), includes several novel and refined coding tools at different levels of the coding chain. These tools bring significant coding gains with respect to the previous standard, high efficiency video coding (HEVC). However, the encoder may still introduce visible coding artifacts, mainly caused by coding decisions applied to adjust the bitrate to the available bandwidth. Hence, pre and post-processing techniques are generally added to the coding pipeline to improve the quality of the decoded video. These methods have recently shown outstanding results compared to traditional approaches, thanks to the recent advances in deep learning. Generally, multiple neural networks are trained independently to perform different tasks, thus omitting to benefit from the redundancy that exists between the models. In this paper, we investigate a learning-based solution as a post-processing step to enhance the decoded VVC video quality. Our method relies on multitask learning to perform both quality enhancement and super-resolution using a single shared network optimized for multiple degradation levels. The proposed solution enables a good performance in both mitigating coding artifacts and super-resolution with fewer network parameters compared to traditional specialized architectures., Comment: accepted as a conference paper to Picture Coding Symposium (PCS) 2021

This paper introduces a novel framework for end-to-end learned video coding. Image compression is generalized through conditional coding to exploit information from reference frames, allowing to process intra and inter frames with the same coder. The system is trained through the minimization of a rate-distortion cost, with no pre-training or proxy loss. Its flexibility is assessed under three coding configurations (All Intra, Low-delay P and Random Access), where it is shown to achieve performance competitive with the state-of-the-art video codec HEVC., Comment: Neural Compression Workshop @ ICLR 2021

The next-generation Versatile Video Coding (VVC) standard introduces a new Multi-Type Tree (MTT) block partitioning structure that supports Binary-Tree (BT) and Ternary-Tree (TT) splits in both vertical and horizontal directions. This new approach leads to five possible splits at each block depth and thereby improves the coding efficiency of VVC over that of the preceding High Efficiency Video Coding (HEVC) standard, which only supports Quad-Tree (QT) partitioning with a single split per block depth. However, MTT also has brought a considerable impact on encoder computational complexity. In this paper, a two-stage learning-based technique is proposed to tackle the complexity overhead of MTT in VVC intra encoders. In our scheme, the input block is first processed by a Convolutional Neural Network (CNN) to predict its spatial features through a vector of probabilities describing the partition at each 4x4 edge. Subsequently, a Decision Tree (DT) model leverages this vector of spatial features to predict the most likely splits at each block. Finally, based on this prediction, only the N most likely splits are processed by the Rate-Distortion (RD) process of the encoder. In order to train our CNN and DT models on a wide range of image contents, we also propose a public VVC frame partitioning dataset based on existing image dataset encoded with the VVC reference software encoder. Our proposal relying on the top-3 configuration reaches 46.6% complexity reduction for a negligible bitrate increase of 0.86%. A top-2 configuration enables a higher complexity reduction of 69.8% for 2.57% bitrate loss. These results emphasis a better trade-off between VTM intra coding efficiency and complexity reduction compared to the state-of-the-art solutions.

Security-sensitive applications that rely on Deep Neural Networks (DNNs) are vulnerable to small perturbations that are crafted to generate Adversarial Examples(AEs). The AEs are imperceptible to humans and cause DNN to misclassify them. Many defense and detection techniques have been proposed. Model's confidences and Dropout, as a popular way to estimate the model's uncertainty, have been used for AE detection but they showed limited success against black- and gray-box attacks. Moreover, the state-of-the-art detection techniques have been designed for specific attacks or broken by others, need knowledge about the attacks, are not consistent, increase model parameters overhead, are time-consuming, or have latency in inference time. To trade off these factors, we revisit the model's uncertainty and confidences and propose a novel unsupervised ensemble AE detection mechanism that 1) uses the uncertainty method called SelectiveNet, 2) processes model layers outputs, i.e.feature maps, to generate new confidence probabilities. The detection method is called Selective and Feature based Adversarial Detection (SFAD). Experimental results show that the proposed approach achieves better performance against black- and gray-box attacks than the state-of-the-art methods and achieves comparable performance against white-box attacks. Moreover, results show that SFAD is fully robust against High Confidence Attacks (HCAs) for MNIST and partially robust for CIFAR10 datasets., Comment: Under review

Light field (LF) technology is considered as a promising way for providing a high-quality virtual reality (VR) content. However, such an imaging technology produces a large amount of data requiring efficient LF image compression solutions. In this paper, we propose a LF image coding method based on a view synthesis and view quality enhancement techniques. Instead of transmitting all the LF views, only a sparse set of reference views are encoded and transmitted, while the remaining views are synthesized at the decoder side. The transmitted views are encoded using the versatile video coding (VVC) standard and are used as reference views to synthesize the dropped views. The selection of non-reference dropped views is performed using a rate-distortion optimization based on the VVC temporal scalability. The dropped views are reconstructed using the LF dual discriminator GAN (LF-D2GAN) model. In addition, to ensure that the quality of the views is consistent, at the decoder, a quality enhancement procedure is performed on the reconstructed views allowing smooth navigation across views. Experimental results show that the proposed method provides high coding performance and overcomes the state-of-the-art LF image compression methods by -36.22% in terms of BD-BR and 1.35 dB in BD-PSNR. The web page of this work is available at https://naderbakir79.github.io/LFD2GAN.html.

Versatile video coding (VVC) is the next generation video coding standard developed by the joint video experts team (JVET) and released in July 2020. VVC introduces several new coding tools providing a significant coding gain over the high efficiency video coding (HEVC) standard. It is well known that increasing the coding efficiency adds more dependencies in the video bitstream making format-compliant encryption with the standard more challenging. In this paper we tackle the problem of selective encryption of the VVC standard in format-compliant and constant bitrate. These two constraints ensure that the encrypted bitstream can be decoded by any VVC decoder while the bitrate remains unchanged by the encryption. The selective encryption of all possible VVC syntax elements is investigated. A new algorithm is proposed to encrypt in format-compliant and constant bitrate the transform coefficients (TCs) together with other syntax elements at the level of the entropy encoder. The proposed solution was integrated and assessed under the VVC reference software model version 6.0. Experimental results showed that the encryption drastically decreases the video quality while the encryption is robust against several types of attacks. The encryption space is estimated in the range of 15% to 26% of the bitstream size resulting in a lightweight encryption process. The web page of this work is available at https://gugautie.github.io/sevvc/.

The Digital Video Broadcasting (DVB) has proposed to introduce the Ultra-High Definition services in three phases: UHD-1 phase 1, UHD-1 phase 2 and UHD-2. The UHD-1 phase 2 specification includes several new features such as High Dynamic Range (HDR) and High Frame-Rate (HFR). It has been shown in several studies that HFR (+100 fps) enhances the perceptual quality and that this quality enhancement is content-dependent. On the other hand, HFR brings several challenges to the transmission chain including codec complexity increase and bit-rate overhead, which may delay or even prevent its deployment in the broadcast echo-system. In this paper, we propose a Variable Frame Rate (VFR) solution to determine the minimum (critical) frame-rate that preserves the perceived video quality of HFR video. The frame-rate determination is modeled as a 3-class classification problem which consists in dynamically and locally selecting one frame-rate among three: 30, 60 and 120 frames per second. Two random forests classifiers are trained with a ground truth carefully built by experts for this purpose. The subjective results conducted on ten HFR video contents, not included in the training set, clearly show the efficiency of the proposed solution enabling to locally determine the lowest possible frame-rate while preserving the quality of the HFR content. Moreover, our VFR solution enables significant bit-rate savings and complexity reductions at both encoder and decoder sides.

VVC is the next generation video coding standard, offering coding capability beyond HEVC standard. The high computational complexity of the latest video coding standards requires high-level parallelism techniques, in order to achieve real-time and low latency encoding and decoding. HEVC and VVC include tile grid partitioning that allows to process simultaneously rectangular regions of a frame with independent threads. The tile grid may be further partitioned into a horizontal sub-grid of Rectangular Slices (RSs), increasing the partitioning flexibility. The dynamic Tile and Rectangular Slice (TRS) partitioning solution proposed in this paper benefits from this flexibility. The TRS partitioning is carried-out at the frame level, taking into account both spatial texture of the content and encoding times of previously encoded frames. The proposed solution searches the best partitioning configuration that minimizes the trade-off between multi-thread encoding time and encoding quality loss. Experiments prove that the proposed solution, compared to uniform TRS partitioning, significantly decreases multi-thread encoding time, with slightly better encoding quality.