This paper presents a comparative evaluation of the V-Nova LCEVC SDK against several practical open-source video encoders, namely SVT-AV1, XEVE, VVenC, x265, and x264. We analyze the trade-offs between the compression efficiency and encoder/decoder runtime of these encoders for high-resolution (UHD and HD) 10-bit consumer applications under a random access configuration. Rate–distortion behavior is assessed using Video Multimethod Assessment Fusion (VMAF, and VMAF-NEG) and Peak Signal-to-Noise Ratio (PSNR), while computational cost is measured through the encoder/decoder runtime. We also analyze the impact of LCEVC’s enhancement layer in terms of both bitrate increase and rate–distortion improvement. The results show that V-Nova LCEVC SDK delivers notable reductions in encoding time with respect to its base codecs, highlighting its suitability as a low-complexity enhancement layer. By comparison, VVenC exhibits a strong compression performance at the expense of high complexity, XEVE also displays considerable encoding times, and SVT-AV1 offers a more balanced compromise between efficiency and computational requirements.

While Versatile Video Coding (VVC) offers unprecedented compression efficiency, its high computational complexity introduces severe challenges for adaptive streaming scenarios. Existing transcoding and multi-rate encoding approaches primarily address cross-standard conversion at a fixed resolution or rely on raw video inputs, leaving a significant research gap in accelerated downscaling directly from compressed bitstreams. To address this limitation, we propose a homogeneous transcoding method for reducing the spatial resolution of video content within the VVC standard. The transcoder reuses partitioning information from the full-resolution bitstream to generate a lower-resolution sequence, thus avoiding a complete repartitioning process. This approach not only allows faster multi-bitrate encoding in streaming applications but also facilitates its integration as a base layer within the Low Complexity Enhancement Video Coding (LCEVC) standard. Experimental results show that the proposed method significantly reduces transcoding time by an average of 73.94% while maintaining perceptual quality. Moreover, the integration of this approach in a VVC-to-LCEVC transcoding pipeline demonstrates a 36.60% reduction in decoding time due to the lower complexity of LCEVC.

Achieving reliable high-fidelity image transmission over dynamic channels remains a critical challenge in modern communication systems, particularly in vehicular environments with significant noise impairments. In response, this paper proposes a novel quantum multiple description coding (Q-MDC) framework to enhance image transmission robustness and reconstruction quality. The proposed approach partitions the source image at the pixel level into odd and even components to generate two primary descriptions, which are subsequently converted into bitstreams using various source encoding techniques, including JPEG, JPEG2000, HEIF, and uncompressed formats. These bitstreams are optionally channel encoded and encoded into quantum states in the computational basis. A third correlated description is generated using a controlled-NOT operation, embedding inter-description redundancy while maintaining equal bandwidth across all descriptions. Each description is transformed into quantum superposition states using a Hadamard-based encoder and transmitted over independent noisy channels. At the receiver, inverse Hadamard operations restore the computational basis representation. A packet- wise and qubit- wise deterministic recovery mechanism is then employed to reconstruct corrupted descriptions by exploiting inter-description dependencies. The recovered descriptions are measured, followed by channel decoding and source decoding to reconstruct the image. Simulation results demonstrate that the proposed Q-MDC framework significantly improves peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality index (UQI) compared to classical MDC and channel-coded single-stream transmission schemes, achieving SNR gains of up to 4 dB and 7 dB, respectively. The results highlight the effectiveness of the proposed approach for robust multimedia transmission in error-prone wireless environments.

Reliable transmission of compressed images over error-prone channels remains challenging because compressed bitstreams are highly sensitive to channel impairments. Although existing quantum-inspired encoding schemes exploit superposition to improve transmission reliability, they remain vulnerable to channel noise, limiting reconstructed image quality. To address these limitations, this paper proposes a chirp transform-based multi-qubit encoding framework for robust compressed image transmission. Unlike conventional transform-based approaches, including the Hadamard transform and Quantum Fourier Transform (QFT), the proposed framework introduces nonlinear (quadratic) phase modulation into the multi-qubit representation, producing a high-dimensional superposition that enhances noise resilience through improved phase separability. This nonlinear phase variation improves error resilience through phase-induced decorrelation, reducing noise-induced distortions. The system integrates Joint Photographic Experts Group (JPEG) and High Efficiency Image File Format (HEIF) source encoding, followed by channel coding, multi-qubit encoding, and chirp transformation before transmission through a simulated quantum channel. At the receiver, inverse chirp transformation, measurement-based decoding, channel decoding, and source decoding reconstruct the transmitted image. Performance is evaluated using Bit Error Rate (BER), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Universal Quality Index (UQI). Experimental results demonstrate that the proposed framework achieves up to a 3 dB signal-to-noise ratio (SNR) gain over QFT-based and Hadamard-based systems in the single-qubit configuration. For the eight-qubit configuration, gains of up to 2 dB over QFT-based systems and 7 dB over Hadamard-based systems are achieved. These results demonstrate the effectiveness and scalability of the proposed framework for robust compressed image transmission over noisy quantum communication channels.

The Versatile Video Coding (VVC) standard was released in 2020 to replace the High Efficiency Video Coding (HEVC) standard, making it necessary to convert HEVC encoded content to VCC to exploit its compression performance, which was achieved by using a larger block size of 128 × 128 pixels, among other new coding tools. However, 80.93% of the encoding time is spent on finding a suitable block partitioning. To reduce this time, this proposal presents an HEVC-to-VVC transcoding algorithm focused on accelerating the CTU partitioning decisions. The transcoder takes different information from the input bitstream of HEVC, and feeds it to two Bayes-based models. Experimental results show a time saving in the transcoding process of 45.40%, compared with the traditional cascade transcoder. This time gain has been obtained on average for all test sequences in the Random Access scenario, at the expense of only 1.50% BD-rate.

With the increased use of mobile devices, and embedded systems, battery life has become of vital importance. Thus, in the video coding field, energy consumption has joined compression efficiency and computational complexity as a key parameter. Energy consumption is one of the most important factors to consider in any video-intensive application due to its relationship to the autonomy of these devices. This article aims to carry out a performance and temperature analysis of two embedded systems (Raspberry Pi 4 and Jetson Nano), which are used in multiple applications and fields, including video encoding, and a consumption study of different encoders of various video standards executed on them. The analysis will consider parameters such as the compression rate, the video quality using an objective metric (PSNR), and the computing time over an intrascenario. In this study, three software encoders and three hardware encoders implementing different video standards have been analyzed: x264 (H.264/AVC), x265 (HEVC), rav1e (AV1), omxh264enc (H.264/AVC), nvv4l2h264enc (H.264/AVC), and nvv4l2h265enc (HEVC). Finally, the results demonstrate that real-time video streaming using hardware encoders is only achievable for the full-HD resolution format.

The release of the latest video coding standard, known as Versatile Video Coding (VVC), has created the need to convert current High Efficiency Video Coding (HEVC) content to this new standard. However, the traditional cascade transcoding pipeline is not effective due to the exorbitant computational complexity of VVC. With this in mind, this paper proposes a fast HEVC-VVC transcoder that implements a probabilistic classifier based on Na?ve-Bayes at the first partitioning level (128 ? 128 pixels). This model uses features extracted from the 128 ? 128 size blocks of the residual and reconstructed frames in the HEVC bitstream, and their correlation with the block partitioning structure. For the subsequent VVC coding depth levels, partitioning decisions are derived from the HEVC structure. The results achieve a 57.08% transcoding time reduction with a BD-rate penalty of 2.40%, compared with a traditional transcoding approach for the random access encoding configuration.

With the advent of smartphones and tablets, video traffic on the Internet has increased enormously. With this in mind, in 2013 the High Efficiency Video Coding (HEVC) standard was released with the aim of reducing the bit rate (at the same quality) by 50% with respect to its predecessor. However, new contents with greater resolutions and requirements appear every day, making it necessary to further reduce the bit rate. Perceptual video coding has recently been recognized as a promising approach to achieving high-performance video compression and eye tracking data can be used to create and verify these models. In this paper, we present a new algorithm for the bit rate reduction of screen recorded sequences based on the visual perception of videos. An eye tracking system is used during the recording to locate the fixation point of the viewer. Then, the area around that point is encoded with the base quantization parameter (QP) value, which increases when moving away from it. The results show that up to 31.3% of the bit rate may be saved when compared with the original HEVC-encoded sequence, without a significant impact on the perceived quality.

With the advent of smartphones and tablets, the amount of online video traffic has increased enormously. This, together with the growing popularity of high-definition video, motivated the development of the High Efficiency Video Coding (HEVC) standard, released in 2013, with the aim of achieving a 50% bitrate reduction with respect to its predecessor, namely H.264/MPEG-4 Advanced Video Coding (AVC). However, new contents with greater resolutions and requirements arise every day, making it necessary to reduce the bitrate to a further extent. In this regard, the efforts to become the leading video codec in the market resulted in two main contenders: the Joint Video Experts Team (JVET), which leads the development of the Versatile Video Coding (VVC) standard, and the Alliance for Open Media (AOMedia), which spearheads the AOMedia Video 1 (AV1) project. In this context, this paper presents a rate-distortion/complexity analysis of HEVC, VVC and AV1 main video codecs using objective measures of assessment in order to analyze their real capabilities. The analysis, which was done using well-defined test conditions, reveals that VVC considerably outperforms both HEVC and AV1 in terms of coding efficiency.

High Efficiency Video Coding (HEVC) is currently the latest video coding standard available on the market, and it is able to offer up to twice the coding efficiency, in the range of 50% bitrate reduction for the same video quality, of the previous standard, namely H.264/Advanced Video Coding (AVC). HEVC was standardized in 2013 for videos up to a resolution of 2K. However, the popularity of 4K videos is increasing due to the growing use of video-on-demand platforms. Therefore, the ITU-T Video Coding Expert Group (VCEG) and the ISO/IEC Moving Picture Expert Group (MPEG) created the Joint Video Exploration Team (JVET) in 2015 to design the future video coding technology under the Joint Exploration Model (JEM), which its latest version achieves an improvement in coding efficiency of 30%, but at a high cost in terms of computational complexity (10×) with respect to HEVC. The new video standard is expected to be ready in 2020, so it is necessary to find efficient mechanisms to convert current content to the new format adopted in JEM. In this regard, our proposal consists in a probabilistic classifier based on Naïve-Bayes that enables the prediction of the splitting decision at the first quadtree level in JEM, reducing the computational complexity of the transcoding process from HEVC to this new standard. The experimental results show a good trade-off between coding efficiency and complexity compared with the anchor transcoder, obtaining a time reduction up to 12.71% at the expense of low coding efficiency penalties in the configurations evaluated.

In the coming years, the Versatile Video Coding (VVC) standard will be launched to replace the current High Efficiency Video Coding (HEVC) standard, making it necessary to find efficient methods to convert existing multimedia content to the new format. However, transcoding is a complex pipeline composed of a decoding and an encoding process that involves long processing times. On the basis of the existing correlation between the block partitioning structures of both standards, this paper presents an HEVC-to-VVC transcoding scheme. The proposed method consists of a Naïve-Bayes classifier that assists the partitioning decision at the first level of quadtree by using features extracted from the $128\times 128$ pixel blocks of the residual and reconstructed frames in HEVC. The experimental results using random access configuration show an average transcoding time reduction of 13.38% at the cost of a compression efficiency loss of 0.32% in terms of BD-rate.

In the last few years new devices with different computational capabilities and network connections has emerged, and this leads to the need for more adaptable video streams. The last two video coding standards, namely H.264/Advanced Video Coding and High Efficiency Video Coding, have defined scalable versions of themselves that provide this adaptability by the use of several layers with different levels of quality, frame rates, and resolutions. Moreover, HEVC introduced hybrid scalability, which allows a non-HEVC base layer (e.g., H.264/AVC) in combination with HEVC enhancement layers, what provides backwards compatibility with older devices. Nevertheless, scalable video coding is very computationally expensive, making it necessary to accelerate the video encoding. This paper presents a fast Coding Unit size algorithm for the combination of hybrid and temporal scalabilities that is based on machine learning. Experimental results show that an acceleration of 54% is achieved at the cost of a slight increment in the bit rate.

The High Efficiency Video Coding (HEVC) standard has shown large improvements in coding efficiency compared with previous standards. In particular, HEVC outperforms H.264/MPEG-4 Advanced Video Coding by up to 50% in terms of bitrate reduction for similar perceptual quality. This improvement is the result of the introduction of new coding tools, which enable the representation of data using fewer bits, but at the cost of long computation times. One of the most significant tools introduced by HEVC is the novel quadtree-based structure called the coding tree unit (CTU), which can be subsequently split into coding units (CUs), prediction units (PUs), and transform units (TUs), providing huge flexibility in the encoding. However, selecting the optimal tree partitioning requires the evaluation of a huge number of possibilities, which constitutes the most complex operation for the encoder. In order to tackle this task, this paper proposes a CTU/CU partitioning algorithm based on a pre-analysis stage. This stage performs a fast motion estimation that provides preliminary information to the encoder, including estimate distortion costs, which enables the building of the least-cost quadtree. On the basis of this tree, the evaluation performed by the encoder is restricted to a subset of PUs. After a thorough statistical analysis of numerous CU/PU combinations, we propose two different configurations that prioritize each of the target variables: coding efficiency and time reduction. In the former case, 58.09% of the encoding time can be saved at the cost of a 2.51% increase in Bjøntegaard delta (BD)-rate, while a larger reduction of 63.08% is achieved with a 3.43% increase in BD-rate in the later case.

ITU-T Video Coding Expert Group and ISO/IEC Moving Picture Expert Group are studying the potential need for standardization of the future video coding technology with a compression capability that significantly exceeds that of the current High Efficiency Video Coding (HEVC) standard, including its current extensions. Both groups are working together on this exploration activity in a collaboration effort known as Joint Video Exploration Team to evaluate compression technology designs proposed by their experts in this area. Preliminary results show that the new model achieves 25% bitrate reduction, but at a cost of extremely high computational complexity (11\(\times {}\)) with respect to HEVC. This paper proposes a pre-analysis algorithm designed to extract motion information of a frame, which is later used in the Motion Estimation (ME) module to speed up the encoder, showing that around 27% of the reference frames can be skipped and that more than 62% of the time is saved in the integer ME operation with a negligible impact of 0.11% in BD-rate.

The high efficiency video coding (HEVC) standard has opened the door to high-quality multimedia contents and new formats such as ultra-high definition as a result of the unceasing demands of the market. This standard is able to outperform prior standards by up to 50% in terms of perceptual video quality, but at the cost of extremely large computational complexities. For this reason, the development of fast coding algorithms is now a requirement to make HEVC an adequate candidate for real-world scenarios. In this regard, this paper proposes a collaborative CPU \(+\) GPU coding architecture for this standard, in which the CPU performs a coarse-grained parallelization of the encoder, while the GPU carries out a fast motion estimation. Given that the GPU algorithm can work together with a wide variety of parallel algorithms, this paper evaluates two of them: tiles, defined in the standard, and slices, already present in previous standards. Results indicate that slices are more adequate in terms of parallel efficiency (10.75\(\times {}\) speedup on average using 12 threads), while tiles achieve better coding efficiency.

The high efficiency video coding (HEVC) is the new video coding standard, which obtains over 50% bit rate savings compared with H.264/AVC for the same perceptual quality. Intra-prediction coding in HEVC achieves high coding performance in expense of high computational complexity, due to the exhaustive evaluation of all available coding units (CU) sizes, with up to 35 prediction modes for each CU, selecting the one with the lower rate distortion cost, among other new features. This paper presents a Unified Architecture to form a novel fast HEVC intra-prediction coding algorithm, denoted as fast partitioning and mode decision. This approach combines a fast partitioning decision algorithm, based on decision trees, which are trained using machine learning techniques, and a fast mode decision algorithm, based on a novel texture orientation detection algorithm, which computes the mean directional variance along a set of co-lines with rational slopes using a sliding window over the prediction unit. Both algorithms proposed apply a similar approach, exploiting the strong correlation between several image features and the optimal CTU partitioning and the optimal prediction mode. The key point of the combined approach is that both algorithms compute the image features with low complexity, and the partition decision and the mode decision can also be taken with low complexity, using decision trees (if-else statements) and by selecting the minimum directional variance between a reduced set of directions. This approach can be implemented using any combination of nodes, obtaining a wide range of time savings, from 44 to 67%, and light penalties from 1.1 to 4.6%. Comparisons with similar state-of-the-art works show the proposed approach achieves the best trade-off between complexity reduction and rate distortion.

High Efficiency Video Coding (HEVC) has become the state-of-the-art video coding standard. It outperforms its predecessors by the introduction of new coding tools, such as the new quadtree-based partitioning scheme called the coding tree unit (CTU), which enables a more flexible partitioning of the input frames. However, selecting the optimal partitioning requires the evaluation of numerous possibilities, which involves long computing times that hinder the applicability of the standard in real-world scenarios. With this in mind, the main focus of this paper is on tackling this complexity by means of a fast partitioning and mode decision algorithm based on a look-ahead stage. This stage performs a preliminary motion estimation that provides the motion costs used to build the least-cost quadtree, which is in turn utilized to conduct the encoding itself. On the basis of this quadtree, the encoder may decide to terminate the partitioning early, or to evaluate additional depth levels adaptively. Furthermore, the encoder may omit some prediction modes according to the costs estimated by the look-ahead stage. A thorough experimental evaluation of the algorithm shows that it can reduce the encoding time by 65.33%, at the expense of only a 1.35% BD-rate for the random access configuration. Combined with a fast inter prediction algorithm, this reduction can rise to 70.55%, while the coding efficiency is maintained at a 1.83% BD-rate. When compared with other related works, these results display an excellent trade-off between the two variables.

The H.264/Advanced Video Coding (AVC) standard has been widely used in the last 10 years, and this has led to a large amount of legacy video being currently encoded in this format. However, the new High Efficiency Video Coding (HEVC) standard has recently been developed and it is thought that it will dominate the market in the next few years. These two facts make an efficient transcoding system between H.264/AVC and HEVC necessary. Moreover, it should be taken into account that intra sequences are commonly used in certain scenarios, such as video editing and post-production, making a migration of these intra contents from H.264/AVC to HEVC necessary. This paper proposes a fast intra H.264/AVC to HEVC transcoding system that is based on Bayesian classifiers and which outperforms other state-of-the-art algorithms, achieving a good complexity reduction with a negligible bit rate increase.

Over the past years, multimedia usage has changed dramatically, with networks and terminals of diverse bandwidths and capabilities coexisting, making an adaptability of the video stream necessary. By the use of video scalability schemes, video streams would be able to adapt to these heterogeneous networks and a wide range of terminals. Moreover, some devices may be able to decode a subset of all the available video standards, e.g. most devices can decode the well-known H.264/advanced video coding (AVC) standard, which has dominated the market for the past 10 years. However, more recent devices can take the advantage of more modern standards whose compression performance is much higher, such as high-efficiency video coding (HEVC). This problem can be solved by the use of hybrid scalability, which allows the use of H.264/AVC for the base layer and HEVC for the enhancement layers. However, scalable video coding is very computationally expensive, so acceleration techniques are of great help in this kind of encoders. This paper presents a fast inter prediction algorithm which makes use of information from H.264/AVC base layer encoding and uses it to make faster decisions in HEVC. Experimental results show that the proposed algorithm can achieve a good tradeoff between coding efficiency and complexity.

High-Efficiency Video Coding (HEVC) was conceived by the Joint Collaborative Team on Video Coding as the natural successor of the H.264/AVC standard, which has been the most extended digital video standard in all segments of the domestic and professional markets for over 10 years. HEVC roughly doubles the compression performance of H.264/AVC in Rate-Distortion terms at the expense of a high computational cost. Thus, most of the previous-generation devices can decode H.264/AVC streams but they cannot decode HEVC yet, while emerging devices are supposed to be able to decode both standards. Video providers should take advantage of bandwidth reduction using HEVC when possible, but they should also provide compatible streams to older devices, making it necessary to encode the same stream using both H.264/AVC and HEVC standards. This paper presents a coding tree unit splitting algorithm for a heterogeneous simultaneous encoding scenario which makes use of information from H.264/AVC encoder to make faster decisions in HEVC. Experimental results show that the proposed algorithm can achieve a good trade-off between coding efficiency and complexity.