For bandwidth-constrained multimedia applications, simultaneously achieving ultra-low bitrate human video compression and accurate vertex prediction remains a critical challenge, as it demands the harmonization of dynamic motion modeling, detailed appearance synthesis, and geometric consistency. To address this challenge, we propose Sparse2Dense, a keypoint-driven generative framework that leverages extremely sparse 3D keypoints as compact transmitted symbols to enable ultra-low bitrate human video compression and precise human vertex prediction. The key innovation is the multi-task learning-based and keypointaware deep generative model, which could encode complex human motion via compact 3D keypoints and leverage these sparse keypoints to estimate dense motion for video synthesis with temporal coherence and realistic textures. Additionally, a vertex predictor is integrated to learn human vertex geometry through joint optimization with video generation, ensuring alignment between visual content and geometric structure. Extensive experiments demonstrate that the proposed Sparse2Dense framework achieves competitive compression performance for human video over traditional/generative video codecs, whilst enabling precise human vertex prediction for downstream geometry applications. As such, Sparse2Dense is expected to facilitate bandwidth-efficient human-centric media transmission, such as realtime motion analysis, virtual human animation, and immersive entertainment.

Substantial Gaussian splatting format point clouds require effective compression. In this paper, we propose Voxel-GS, a simple yet highly effective framework that departs from the complex neural entropy models of prior work, instead achieving competitive performance using only a lightweight rate proxy and run-length coding. Specifically, we employ a differentiable quantization to discretize the Gaussian attributes of Scaffold-GS. Subsequently, a Laplacian-based rate proxy is devised to impose an entropy constraint, guiding the generation of high-fidelity and compact reconstructions. Finally, this integer-type Gaussian point cloud is compressed losslessly using Octree and run-length coding. Experiments validate that the proposed rate proxy accurately estimates the bitrate of run-length coding, enabling Voxel-GS to eliminate redundancy and optimize for a more compact representation. Consequently, our method achieves a remarkable compression ratio with significantly faster coding speeds than prior art. The code is available at https://github.com/zb12138/VoxelGS.

Template-generated videos (TGVs), created by applying animation templates to static images, have become increasingly prevalent, producing massive user-generated content with highly consistent motion patterns. However, existing video compression schemes are designed to eliminate motion redundancy within individual videos, while overlooking the shared motion patterns widespread across TGVs. To address this limitation, we propose a novel compression scheme that effectively leverages inter-video motion priors to enhance the compression efficiency of TGVs. Specifically, the proposed scheme operates as a two-stage pipeline. In the first stage, high-quality motion priors are identified from a representative TGV based on spatial texture and prediction error. In the second stage, these motion priors are intelligently integrated to expand the motion representation space beyond the local candidate lists in Merge and AMVP modes, thereby enabling the codec to remove inter-video redundancy. Experimental results on the versatile video coding test model (VTM-23.0) demonstrate consistent coding gains across various compression scenarios for TGVs, achieving average BD-rate savings of$1.07 \%, 1.38 {\%}$, and 1.18% under low-delay P (LDP), low-delay B (LDB), and random access (RA) configurations, respectively.

This paper proposes an efficient 3D avatar coding framework that leverages compact human priors and canonical-to-target transformation to enable high-quality 3D human avatar video compression at ultra-low bit rates. The framework begins by training a canonical Gaussian avatar using articulated splatting in a network-free manner, which serves as the foundation for avatar appearance modeling. Simultaneously, a human-prior template is employed to capture temporal body movements through compact parametric representations. This decomposition of appearance and temporal evolution minimizes redundancy, enabling efficient compression: the canonical avatar is shared across the sequence, requiring compression only once, while the temporal parameters, consisting of just 94 parameters per frame, are transmitted with minimal bit-rate. For each frame, the target human avatar is generated by deforming canonical avatar via Linear Blend Skinning transformation, facilitating temporalcoherent video reconstruction and novel view synthesis. Experimental results demonstrate that the proposed method significantly outperforms conventional 2D/3D codecs and existing learnable dynamic 3D Gaussian splatting compression method in terms of rate-distortion performance on mainstream multi-view human video datasets, paving the way for seamless immersive multimedia experiences in meta-verse applications.

Latent diffusion models (LDMs) have emerged as a prominent framework for image generation, consisting of a diffusion model$\mathcal{M}$and a VAE decoder$\mathcal{D}$. High-quality image generation models are large and computationally intensive. As a result, image generation is typically performed on cloud servers, with the generated images then transmitted to edge devices.

Recently, deep generative models have greatly advanced the progress of face video coding towards promising rate-distortion performance and diverse application functionalities. Beyond traditional hybrid video coding paradigms, Generative Face Video Compression (GFVC) relying on the strong capabilities of deep generative models and the philosophy of early Model-Based Coding (MBC) can facilitate the compact representation and realistic reconstruction of visual face signal, thus achieving ultra-low bitrate face video communication. However, these GFVC algorithms are sometimes faced with unstable reconstruction quality and limited bitrate ranges. To address these problems, this paper proposes a novel Progressive Face Video Compression framework, namely PFVC, that utilizes adaptive visual tokens to realize exceptional trade-offs between reconstruction robustness and bandwidth intelligence. In particular, the encoder of the proposed PFVC projects the high-dimensional face signal into adaptive visual tokens in a progressive manner, whilst the decoder can further reconstruct these adaptive visual tokens for motion estimation and signal synthesis with different granularity levels. Experimental results demonstrate that the proposed PFVC framework can achieve better coding flexibility and superior rate-distortion performance in comparison with the latest Versatile Video Coding (VVC) codec and the state-of-the-art GFVC algorithms. The project page can be found at https://github.com/Berlin0610/PFVC.

This paper proposes a highly efficient sample adaptive offset (SAO) mode decision algorithm. By leveraging both the directional correlations between the SAO and intra-prediction decisions, and the SAO decisions' spatial correlations, the SAO mode candidates are effectively pruned during the rate-distortion optimization process, accelerating the SAO encoding process with negligible BD-rate loss.

Image Compression for Machines (ICM) aims to compress images for machine vision tasks, while current methods mostly focus on the demands for high-level tasks. However, the quality of original images is usually not guaranteed in the real world, leading to even worse downstream task performance after compression. Thus, lowlevel (LL) restoration tasks should also be considered in ICM. In this paper, we propose the first ICM framework for LL machine vision tasks, namely LL-ICM, which optimizes the compression and LL processing performance simultaneously. Moreover, LL-ICM leverages large vision-language model (VLM) to solve different LL task within a single model, which is particularly useful when the distortion type of the original image is uncertain. As illustrated in Fig. 1(a), LL-ICM consists of a neural image codec and a VLM-based LL processing module. Given an original image with distortions, LL-ICM firstly compress it as$\hat{\mathbf{X}}$. Then, we extract a generalized feature F from$\hat{\mathbf{X}}$, which is then encoded as two representations, distortion type$\varphi$and caption$\sigma$. After that, the LL processing module receives$\hat{\mathbf{X}}$and its representations to generate the restored version of$\hat{\mathbf{X}}$, i.e.,$\hat{\mathbf{X}}_{\mathbf{H}}$.

This paper proposes to learn generative priors from the motion patterns instead of video contents for generative video compression. The priors are derived from small motion dynamics in common scenes such as swinging trees in the wind and floating boat on the sea. Utilizing such compact motion priors, a novel generative scene dynamics compression framework is built to realize ultra-low bit-rate communication and high-quality reconstruction for diverse scene contents. At the encoder side, motion priors are characterized into compact representations in a dense-to-sparse manner. At the decoder side, the decoded motion priors serve as the trajectory hints for scene dynamics reconstruction via a diffusion based flow-driven generator. The experimental results illustrate that the proposed method can achieve superior rate-distortion performance and outperform the state-of-the-art conventional video codec Versatile Video Coding (VVC) on scene dynamics sequences.

Sensor data analysis is a crucial task for environmental cognition in smart traffic systems. Recently, vehicle-to-everything (V2X) collaborative analysis has leveraged intermediate feature communication between vehicles and infrastructure to achieve superior analysis performance compared to single-vehicle approaches. However, due to the limited bandwidth of V2X communication links, directly transmitting features can be inefficient, resulting in significant delays that are unacceptable for real-time decision-making. To address this challenge, we propose a compact feature representation method in the bird's eye view (BEV) space for communication-efficient collaborative analysis. As shown in Fig. 1, the proposed method can be viewed as a task-aware distributed coding approach with decoder side information. First, the ego vehicle and the networked infrastructure convert raw LiDAR data into BEV features using a shared PointPillars feature extractor. The infrastructure then applies the proposed BEV codec to transform these BEV features into a compact representation, encoding them into a binary bitstream through entropy coding based on the estimated distribution. The received features are subsequently warped and fused with the ego vehicle's features using a bidirectional attention fusion module, and processed by a single-shot detector to perform 3D object detection. Experimental results on the DAIR-V2X-C dataset demonstrate that the proposed framework achieves more than 1000 times compression compared to directly transmitting floating-point features, while maintaining high analysis performance in real-world V2X scenarios.

Generative Face Video Coding (GFVC) techniques can exploit the compact representation of facial priors and the strong inference capability of deep generative models, achieving high-quality face video communication in ultra-low bandwidth scenarios. This paper conducts a comprehensive survey on the recent advances of the GFVC techniques and standardization efforts, which could be applicable to ultra low bitrate communication, user-specified animation/filtering and metaverse-related functionalities. In particular, we generalize GFVC systems within one coding framework and summarize different GFVC algorithms with their corresponding visual representations. Moreover, we review the GFVC standardization activities that are specified with supplemental enhancement information messages. Finally, we discuss fundamental challenges and broad applications on GFVC techniques and their standardization potentials, as well as envision their future trends. The project page can be found at https://github.com/Berlin0610/Awesome-Generative-Face-Video-Coding.

Recent advances in generative compression methods have demonstrated remarkable progress in enhancing the perceptual quality of compressed data, especially in scenarios with low bitrates. However, their efficacy and applicability to achieve extreme compression ratios (< 0.05 bpp) remain constrained. In this work, we propose a simple yet effective coding framework by introducing vector quantization (VQ)–based generative models into the image compression domain. The main insight is that the codebook learned by the VQGAN model yields a strong expressive capacity, facilitating efficient compression of continuous information in the latent space while maintaining reconstruction quality. Specifically, an image can be represented as VQ-indices by finding the nearest codeword, which can be encoded using lossless compression methods into bitstreams. We propose clustering a pre-trained large-scale codebook into smaller codebooks through the K-means algorithm, yielding variable bitrates and different levels of reconstruction quality within the coding framework. Furthermore, we introduce a transformer to predict lost indices and restore images in unstable environments. Extensive qualitative and quantitative experiments on various benchmark datasets demonstrate that the proposed framework outperforms state-of-the-art codecs in terms of perceptual quality-oriented metrics and human perception at extremely low bitrates (≤ 0.04 bpp). Remarkably, even with the loss of up to 20% of indices, the images can be effectively restored with minimal perceptual loss.

In this paper, we present a Conv-Attention, a decoder-friendly attention mechanism, in an effort to advancing the practical application of the artificial intelligence-based image coding. More specifically, the proposed method is tailored for JPEG AI, which is the latest advanced neural-network based image coding standard. By identifying the obstacles by profiling the decoding complexity of JPEG AI, the attention module accounts for a significant proportion, which mainly attributes to the intricate network structure and involvement of less efficient operations. Conv-Attention model is composed with plain convolution and activation computations, equipping with sub-scaling and up-scaling design, such that the non-adjacent features can be well captured, leading to the reduction of decoding complexity and maintenance of the synthesis and attentive capability. Simulation results verify the effectiveness of the proposed method with JPEG AI reference software, wherein the decoding complexity is reduced by 80% with negligible coding performance loss. The proposed method was adopted in the 100th JPEG meeting.

Generative face video coding (GFVC) can achieve high-quality visual face communication at ultra-low bit-rate ranges via strong facial prior learning and realistic generation. However, different kinds of feature representations hinder the interoperability of GFVC, as the bitstream generated from one type of feature representation can only be correctly understood by the corresponding decoder. In this paper, we make the first attempt to propose a face feature transcoding framework that enables translatability in GFVC. By integrating a face feature transcoder at the decoder side, received face features can be translated to decoder-specific ones for subsequent face reconstruction. Furthermore, the translation between different types of face features can be achieved using a unified transcoding framework, facilitating seamless interoperability between different facial representations and their associated decoders. Experimental results demonstrate that three main-stream GFVC codecs, each utilizing different face features, can be effectively adapted to one another while retaining promising coding performance, largely extending the generality of the GFVC system. The project page can be found at https://github.com/xyzysz/GFVC_Software-Decoder_Interoperability.

We extend the beam-search based joint rate-distortion optimization (BSJRDO) [1] to High Efficiency Video Coding (HEVC) and investigate the impact of different decisions on it. In BSJRDO, unlike the greedy search, which only maintains one locally optimal path at each decision stage, multiple paths are kept as candidates for future referencing.

Secondary transform was adopted into the third generation Audio Video coding Standard (AVS3) to improve the intra-coded residual coding by applying a 4×4 secondary transform kernel. However, the adaptability of the single 4×4 transform kernel is limited for various residual data. In order to achieve higher residual coding gains, we propose a Decoder-side Secondary Transform Derivation (DSTD) method. Specifically, DSTD expands the maximum range of secondary transform from 4×4 to 8×8, where an 8×8 size transform kernel is introduced to further enhance the capability of compacting residuals. In particularly, three flipped secondary transform types are employed to extend transform candidates, including horizontal, vertical and diagonal flipping types. The boundary continuity is utilized to derive the transform type. Experimental results show that the proposed method can achieve 0.51% and 0.18% BD-rate savings on average under All Intra (AI) and Random Access (RA) configurations, respectively. DSTD has been adopted into the Exploration Video Model (EVM) for AVS4.

With the exponential growth of multimedia in various forms, the volume of acquired visual data has dramatically increased while their value intensity remains relatively low. This presents significant challenges in multimedia search and analytics. In this tutorial, we aim to introduce recent advances of compact visual data representation techniques that enable efficient, flexible, and reliable multimedia search and analytics. We will explore the shift from traditional visual information representation techniques, such as video coding, to biologically inspired information processing paradigms, like digital retina based coding and representation. We will also discuss the representation of point cloud data and Artificial Intelligence Generated Content (AIGC) data, which are becoming increasingly popular in modern machine vision technologies. Additionally, we will discuss the recent advances in quality assessment technologies for multimedia signals under various novel and challenging scenarios. Finally, we will introduce the recent standardization activities in media coding including Video Coding for Machine (VCM). This tutorial aims to stimulate fruitful discussions, encourage innovative research, and drive advancements in the field of semantic and visual communication, multimedia search, analytics, computing as well as generative AI.

Deep neural networks (DNN) have demonstrated unprecedented success for various applications. However, due to the issue of limited dataset availability and the strict legal and ethical requirements for data privacy protection, the broad applications of DNN (e.g., medical imaging classification) with large-scale training data have been largely hindered, greatly constraining the model generalization capability. In this paper, we aim to tackle this problem by developing the privacy-preserving constrained domain generalization method, aiming to improve the generalization capability under the privacy-preserving condition. In particular, we propose to improve the information aggregation process on the centralized server side with a novel gradient alignment loss, expecting that the trained model can be better generalized to the “unseen” but related data. The rationale and effectiveness of our proposed method can be explained by connecting our proposed method with the Maximum Mean Discrepancy (MMD) which has been widely adopted as the distribution distance measure. Experimental results on three domain generalization benchmark datasets indicate that our method can achieve better cross-domain generalization capability compared to the state-of-the-art federated learning methods.

Point clouds offer the realistic three-dimensional (3-D) representation of objects or scenes at the expense of high data volume. To compactly represent such data in real-world applications, Video-based Point Cloud Compression (V-PCC) converts them into two-dimensional (2-D) attribute maps before lossy compression. However, the coding artifacts introduced in the decoded attribute maps eventually bring texture degradation in the reconstructed point cloud. In this paper, we propose a deep-learning based attribute map enhancement method by fully leveraging the guidance of the occupancy map in local feature modification and non-local attention for capturing long-range spatial correlations.

We propose a talking face video compression framework by implicitly transforming the temporal evolution into compact feature representation. More specifically, the temporal evolution of faces, which is complex, non-linear and difficult to extrapolate, is modelled in an end-to-end inference framework based upon very compact features. This enables the high-quality rendering of the face videos, which benefits from the learning of dense motion map with compact feature representation. Therefore, the proposed framework can accommodate ultra-low bandwidth video communication and maintain the quality of the reconstructed videos. Experimental results demonstrate that compared with the state-of-the-art video coding standard Versatile Video Coding (VVC) as well as the latest generative compression scheme Face Video-to-Video Synthesis (Face_vid2vid), the proposed scheme is superior in terms of both objective and subjective quality assessment methods.

Recently, there has been a tremendous demand for high-efficiency face video communications, coinciding with the popularization of the digital human character in numerous applications. This paper demonstrates a new communication paradigm of 3D human digital characters in ultra low-bit-rate application scenarios. The paradigm is grounded on the mild assumption of the consistency and persistence of human ap-pearance, such that only the compact features that determine the pose and expression of the 3D character need to be transmitted. The proposed is also expected to benefit virtual-physical world interaction in Metaverse.

In this paper, we concentrate on the super-resolution (SR) of compressed screen content video, in an effort to address the real-world challenges by considering the underlying characteristics of screen content. Firstly, we propose a new dataset for the SR of screen content video with different distortion levels. Meanwhile, we design an efficient SR structure that could capture the characteristics of compressed screen content video and manipulate the inner-connections in consecutive compressed low-resolution frames, facilitating the high-quality recovery of the high-resolution counter-part. Moreover, we design a new loss function for network training to better remedy the compression distortion and perceptual distortion. Experimental results demonstrate the effectiveness and superiority of the proposed method.

Cross-component prediction, which takes advantage of inter-channel correlations, predicts the chroma block with the luma reconstructed block according to associated linear model. Instead of involving all available reference samples in building the linear model, in this paper, we propose a sub-sampled approach that utilizes at most four neighboring chroma samples and their corresponding down-sampled luma samples, leading to significantly reduced operations in the derivation of model parameters at both encoder and decoder. The proposed scheme is hardware friendly in terms of the overheads of memory access and clock cycles, and greatly benefits the practical implementations of the emerging video coding standard in real applications. Extensive experiments reveal that the proposed sub-sampled method provides simple operations and robust coding performance, leading to the adoption by Versatile Video Coding (VVC) Standard and the third generation Audio Video Coding Standard (AVS3).

Rate distortion optimization (RDO) is the basis for algorithm optimization in video coding [1], such as mode decision, rate control and etc. Minimizing the rate distortion coding cost is usually employed to determine the optimal coding parameters such as quantization level, coding mode, and etc. However, rate and distortion calculations for optimal solution decision from massive possible candidates suffer from dramatically high computation complexity. To resolve this problem, this paper proposes a fast TU level rate model with higher accuracy by fully imitating the behavior pattern hid in entropy.

In this paper, we focus on computationally modeling of the local texture correlations, in an effort to better explore the coding modes with higher priorities in the rate-distortion optimized intra coding. In particular, strong correlations and continuities of local texture with neighboring blocks have been revealed in our analysis, and empirical justifications provide us inspirations on the joint optimization of rate-distortion-complexity when angular modes become finer to adapt the local textures. We examine the philosophy with extensive experiments conducted for refining the intra full-RD list. The results show that better coding performance with on average 0.72% and 3.00% BD-Rate savings for the natural scene and screen content sequences can be achieved in AVS3 test model HPM-5.0 under all intra configuration, with negligible encoding and decoding time variations.

The quad-tree plus binary-tree (QTBT) coding unit (CU) partitioning structure, which has been adopted to the next generation video coding standard, shows promising coding performance when compared with the conventional quad-tree structure in HEVC. In this paper, we propose the Extended Quad-tree (EQT) partitioning, which further extends the QTBT scheme and increases the partitioning exibility. More specifcally, EQT splits a parent CU into four sub-CUs of dierent sizes, which can adequately model the local image content that cannot be elaborately characterized with QTBT. Meanwhile, EQT partitioning allows the interleaving with BT partitioning for enhanced adaptability. Experimental results on the JEM7-QTBT-Only platform show that EQT brings better coding performance with 3.17%, 3.20% and 3.06% BD-Rate gains under random access, low-delay P and low-delay B configurations, respectively.

Motion compensation plays a key role in high efficiency video coding. The popular video compression standards, such as H.264/AVC and HEVC, adopt block based motion compensation technique due to its high compression efficiency and relatively low computational complexity. However, block based motion compensation may not be in accordance with the actual object boundary, potentially leading to low prediction accuracy especially in the high-texture areas. In this paper, we propose a locally refined motion compensation method to address this issue. In particular, the image segmentation is applied on the prediction block indicated by a motion vector rather than the original block to avoid explicit signaling. Furthermore, the local content is analyzed to select one segmented region and subsequently the prediction of this region is generated based on the local motion filed. Experimental results show that the proposed algorithm can achieve 0.8%, 1.1% and 1.7% bitrate savings for Random Access, Lowdelay-B and Lowdelay-P configurations respectively without introducing noticeable computational complexity.

With emerging demand for large-scale video analysis, the Motion Picture Experts Group (MPEG) initiated the Compact Descriptor for Video Analysis (CDVA) standardization in 2014. In this work, we develop novel deep-learning features and incorporate them into the well-established CDVA evaluation framework to study its effectiveness in video analysis. In particular, we propose a Nested Invariance Pooling (NIP) method to obtain compact and robust Convolutional Neural Network (CNNs) descriptors. The CNNs descriptors are generated by applying three different pooling operations to the feature maps of CNNs in a nested way towards rotation and scale invariant feature representation. In particular, the rational, advantages and performance on the combination of CNNs and handcrafted descriptors are provided to better investigate the complementary effects of deep learnt and handcrafted features. Extensive experimental results show that the proposed CNNs descriptors outperform both state-of-the-art CNNs descriptors and canonical handcrafted descriptors adopted in CDVA Experimental Model (CXM) with significant mAP gains of 11.3% and 4.7%, respectively. Moreover, the combination of NIP derived deep invariant descriptors and handcrafted descriptors not only fulfills the lowest bitrate budget of CDVA, but also significantly advances the performance of CDVA core techniques.

Block partition structure has been recognized as a crucial module in video coding scheme. Recently, a quadtree plus binary tree (QTBT) block partition structure has been proposed in the Joint Video Exploration Team (JVET) development. Compared to the quadtree structure in HEVC, QTBT can achieve better coding performance with hugely increased encoding complexity. Here, we propose an effective QTBT partition decision algorithm to achieve a good trade-off between computational complexity and coding performance. In particular, at the Coding Tree Unit level, the partition parameters of QTBT are dynamically derived to adapt to the local characteristics without transmitting any overhead. Subsequently, at the Coding Unit level, a joint-classifier decision tree structure is designed to eliminate unnecessary iterations and meanwhile control the risk of false prediction. Experimental results show that the proposed algorithm can achieve 64% encoding time reduction on average with only 1.26% increase in terms of bit rate. This greatly benefits the practical implementations of QTBT in real application scenarios.

Sparse representation is efficient to approximately recover signals by a linear composition of a few bases from an over-complete dictionary. However, in the scenario of data compression, its efficiency and popularity are hindered due to the extra overhead for encoding the sparse coefficients. Therefore, how to establish an accurate rate model in sparse coding and dictionary learning becomes meaningful, which has been not fully exploited in the context of sparse representation. According to the Shannon entropy inequality, the variance of data source can bound its entropy, thus can reflect the actual coding bits. Therefore, a Globally Variance-Constrained Sparse Representation (GVCSR) model is proposed, where a variance-constrained rate term is introduced to the conventional sparse representation. To solve the non-convex optimization problem, we employ the Alternating Direction Method of Multipliers (ADMM) for sparse coding and dictionary learning, both of which have shown state-of-the-art rate-distortion performance in image representation.

Visual feature descriptors have been successfully deployed in a wide range of applications, e.g. visual retrieval and analysis. To transmit these descriptors over bandwidth-limited networks, a high efficiency feature coding technique is highly desired to maximize compression capability and achieve compact feature representations. In this paper, a hybrid visual feature descriptor compression framework is presented and implemented in the encoding and decoding loops of texture videos. In particular, the multiple-hypothesis prediction is employed to effectively remove redundancies originated not only from spatial and temporal similarities, but also from reconstructed video frames. As the ultimate purpose of the transmitted descriptors is retrieval, the rate-accuracy optimization (RAO) technique is proposed to obtain the best tradeoff between the rate and retrieval performance. Such paradigm enables the conventional video stream to achieve high efficient retrieval/analysis with very low bitrate consumption. Moreover, we also demonstrate that texture video compression can also benefit from the additional information provided by the transmitted descriptors, leading to significantly improvement of coding efficiency on top of the high efficiency video coding (HEVC) standard. Extensive simulations have shown that the proposed method can offer significant bitrate reduction in representing both the descriptors and texture video frames, and meanwhile providing desirable retrieval performance.

The emerging High Efficiency Video Coding (HEVC) standard has improved the coding efficiency drastically, and can provide equivalent subjective quality with more than 50% bit rate reduction compared to its predecessor H.264/AVC. As expected, the improvement on coding efficiency is obtained at the expense of more intensive computation complexity. In this paper, based on an overall analysis of computation complexity in HEVC encoder, a low complexity rate distortion optimization (RDO) coding scheme is proposed by reducing the number of available candidates for evaluation in terms of the intra prediction mode decision, reference frame selection and CU splitting. With the proposed scheme, the RDO technique of HEVC can be implemented in a low-complexity way for complexity-constrained encoders. Experimental results demonstrate that, compared with the original HEVC reference encoder implementation, the proposed algorithms can achieve about 30% reduced encoding time on average with ignorable coding performance degradation (0.8%).