This paper proposes the integration of residual blocks into neural representation for videos (NeRV)-based architectures with the aim of enhancing the reconstruction of detailed patterns and high-level features. Additionally, a coding pipeline is introduced, placing the implicit neural decoder in a real-life video streaming framework. Indeed, DeepCABAC is employed for model compression, applying a quantization scheme followed by the context-adaptive binary arithmetic coding (CABAC) entropy coding algorithm, ultimately leading to bitstream generation. Our method outperforms NeRV, as well as x264 and x265, achieving BD-rate gains against NeRV: $-12.06 \%$ using PSNR and $-14.25 \%$ using MS-SSIM. Furthermore, it exhibits superior subjective quality compared to NeRV, attributed to enhanced high-level feature reconstruction. This observed behavior encourages the application of our method to other NeRV-based models, such as E-NeRV.

The proposed demonstration combines several commercially viable state-of-the-art technologies to achieve significant energy reduction while delivering HDR video without compromising QoE. The components of the demo are: (i) The demo is based our solution on VVC, the most efficient, highest quality video codec available today. (ii) We leverage Advanced HDR by Technicolor, a solution that uniquely can deliver HDR and SDR video using a single stream. (iii) We introduce a solution for dynamically adjusting peak brightness of displays via display adaptation controlled through the network using dynamic metadata encapsulated in MPEG SEI messages. This solution has been tested and shows a capability of reducing power by 10% or more without any subjective quality loss, and far more with minimal subjective impact. (iv) While we demonstrate a streaming (unicast) scenario in our live demo, our system is also capable of working in a dynamic unicast/multicast environment based on DVB transmission standards. (v) The entire demonstration is resulting from an industrial collaboration and is based on ready to market technologies and prototypes.

The integration of advanced video codecs into the streaming pipeline is growing in response to the increasing demand for high quality video content. However, the significant computational demand for advanced codecs like Versatile Video Coding (VVC) poses challenges for service providers, including longer encoding time and higher encoding cost. This challenge becomes even more pronounced in streaming, as the same content needs to be encoded at multiple bitrates (also known as representations) to accommodate different network conditions. To accelerate the encoding process of multiple representations of the same content in VVC, we employ the encoding map of a single representation, known as the reference representation, and utilize its partitioning structure to accelerate the encoding of the remaining representations, referred to as dependent representations. To ensure compatibility with parallel processing, we designate the lowest bitrate representation as the reference representation. The experimental results indicate a substantial improvement in the encoding time for the dependent representations, achieving an average reduction of 40%, while maintaining a minimal average quality drop of only 0.43 in Video Multi-method Assessment Fusion (VMAF). This improvement is observed when utilizing Versatile Video Encoder (VVenC), an open and optimized VVC encoder implementation.

The promising compression efficiency improvement of Versatile Video Coding (VVC) compared to High Efficiency Video Coding (HEVC) [1] comes at the cost of a non-negligible encoder-side complexity. The largely increased complexity overhead is a possible obstacle towards its industrial implementation. Many papers have proposed acceleration methods for VVC. Still, a better understanding of VVC complexity, especially related to new partitions and coding tools, is desirable to help the design of new and better acceleration methods. For this purpose, statistical analyses have been conducted, with a focus on Coding Unit (CU) sizes and inter coding modes.

Modern video compression solutions rely on intra-prediction to exploit spatial redundancy within a frame. In this paper, a new intra-prediction scheme is presented whereby one or more prediction directions are inferred at the decoder side by means of a texture analysis process. The inferred directions are then combined by means of prediction fusion, to compute the final predicted samples. No further information is required in the bitstream, considerably limiting the amount of bits necessary to be transmitted in order to derive the prediction block at the decoder. Experiments in the context of the state-of-the-art Versatile Video Coding standard show the proposed method can improve coding efficiency by -0.63% BD-rates on average, and up to -1.04% on larger resolution sequences, with limited impact on the decoder complexity.

Following the finalization of the state-of-the-art video compression standard High Efficiency Video Coding (HEVC) in 2013, there is a growing need for more compression efficiency to handle various new video services and formats. In order to address this need, the ITU-T and MPEG standardization bodies are working together in a Joint Video Expert Team (JVET) group on the standardization of a new video codec called Versatile Video Coding (VVC), aiming to achieve 50% bitrate reduction over HEVC by 2020. One of the solutions that can be proposed in the intra mode is increasing the number of prediction modes, thus allowing more efficient predictions and less energetic residues. However, this solution causes an increase in the signaling cost of the chosen mode, which counterbalances some of the gains obtained by the newly added prediction modes. To address this problem, we propose to derive prediction modes at the decoder, to avoid sending them in the bitstream. This is done by deriving the best prediction mode at the encoder through a gradient calculation performed on each pixel of a well-determined causal region of the image. This method is identically applied at the decoder, thus avoiding the intra mode signaling in the bitstream. This process defines a new coding mode named Decoder side Intra Mode Derivation (DIMD), which competes with other coding modes normally tested at the encoder, including the classical intra coding mode. The proposed method reduces, on average 0.3%, the bitrate used to encode a sequence configured in All Intra (AI), with limited additional decoding complexity (2%).

The standardization of the next generation video codec, called Versatile Video Coding (VVC), has been progressing fast since 2018. At the same video quality, the VVC Test Model (VTM) currently provides about 27% bitrate saving compared to the preceding High Efficiency Video Coding (HEVC) standard. The ultimate objective is to reach about 50% improvement by the end of 2020. This paper presents an intra coding algorithm for VTM that skips the mode signaling at the encoder side and leaves it to be derived at the decoder side. To this end, a Histogram of Gradient (HoG) is computed from the texture of the previously reconstructed pixels and processed to derive the intra mode. Moreover, the proposed method is tuned to minimize its side-effects on the existing intra coding tools. Experimental results, on top of VTM-3.0, show that the proposed algorithm brings -0.22% BD-rate gain, with a negligible complexity.

This paper presents a novel inter-view motion prediction technique used in 3D-HEVC which provides efficient compression for motion vectors. The 3D extension of HEVC (High Efficiency Video Coding) namely 3D-HEVC is under development in JCT-3V for coding multi-view video and depth data. Inter-view motion prediction takes benefit of the inter-view correlation between views by inferring the motion information of a view from the already coded motion information in another view as well as the local disparity between two views. Experimental results show that inter-view motion prediction in 3D-HEVC provides an average bit-rate savings of 15% for the dependent views.

This paper addresses the issue of robust and joint source-channel decoding of quasiarithmetic codes. Quasiarithmetic coding is a reduced precision and complexity implementation of arithmetic coding. This paper provides first a state model of a quasiarithmetic decoder for binary and M-ary sources. The design of an error-resilient soft decoding algorithm follows quite naturally. The compression efficiency of quasiarithmetic codes allows to add extra redundancy in the form of markers designed specifically to prevent de-synchronization. The algorithm is directly amenable for iterative source-channel decoding in the spirit of serial turbo codes. The coding and decoding algorithms have been tested for a wide range of channel signal-to-noise ratios. Experimental results reveal improved SER and SNR performances against Huffman and optimal arithmetic codes.

This paper addresses the issue of robust decoding of arithmetic codes. We first analyze dependencies between the variables involved in arithmetic coding by means of the Bayesian formalism. This provides a suitable framework for designing a soft decoding algorithm that provides high error-resilience. It also provides a natural setting for "soft synchronization", i.e., to introduce anchors favoring the likelihood of "synchronized" paths. In order to maintain the complexity of the estimation within a realistic range, a simple, yet efficient, pruning method is described. Models and algorithms are then applied to context-based arithmetic coding widely used in practical systems (e.g. JPEG-2000). Experimentation results with both theoretical sources and with real images coded with JPEG-2000 reveal very good error resilience performances.

This paper addresses the issue of robust and joint source-channel decoding of arithmetic codes. We first analyze dependencies between the variables involved in arithmetic coding by means of the Bayesian formalism. This provides a suitable framework for designing a soft decoding algorithm that provides high error-resilience. It also provides a natural setting for "soft synchronization", i.e., to introduce anchors favoring the likelihood of "synchronized" paths. In order to maintain the complexity of the estimation within a realistic range, a simple, yet efficient, pruning method is described. The algorithm can be placed in an iterative source-channel decoding structure, in the spirit of serial turbo codes. Models and algorithms are then applied to context-based arithmetic coding widely used in practical systems (e.g., JPEG-2000). Experimentation results with both theoretical sources and with real images coded with JPEG-2000 reveal very good error resilience performances.

A multiple description scalar quantization (MDSQ) based coding system can be regarded as a source coder (quantizer) followed by a channel coder, i.e. the combination of index and codeword assignment. The redundancy, or the correlation between the descriptions, is controlled by the number of diagonals covered by the index assignment. In this paper, we analyse dependencies between the variables involved in the MDSQ coding chain and design an estimation strategy making use of part of the global model of dependencies at each time. Inference of the hidden states of the connected models is done by applying belief propagation principles on the resulting Bayesian network. We try to evidence the most appropriate form of redundancy one should introduce in the context of variable length code compressed streams in order to fight against de-synchronizations when impaired by channel noise.

A multiple description scalar quantization (MDSQ) based coding system can be regarded as a source coder (quantizer) followed by a channel coder, i.e. the combination of index and codeword assignment. The redundancy, or the correlation between the descriptions, is controlled by the number of diagonals covered by the index assignment. We consider here the usage of multiple description uniform scalar quantization (that we call MDUSQ) for robust and progressive transmission of images over unreliable channels. The progressive feature is an important factor for rate control in non-stationary (varying bandwidth) communication environments. In this context, the paper describes an embedded index assignment strategy that provides improved rate-distortion performances in progressive transmission scenarios, against index assignments defined so far for MDSQ. The MDUSQ together with the embedded index assignment algorithm are incorporated into the JPEG2000 verification model. The approach is compared against a progressive multiple description scheme based on a polyphase transform (PT) decomposition of the signal.