In this paper, the quality evaluation of the responses to the Call for Proposals (CfP) of JPEG Pleno Point Cloud Coding is presented. Three responses to the CfP were evaluated together with the state of the art anchor codecs G-PCC and VPCC from MPEG. The JPEG committee selected a set of eight point clouds that were encoded at different pre-established bitrates. For the subjective evaluation of the responses to the CfP, a set of video sequences were created where the reference and distorted decoded point clouds were rotated about their axes side by side. Furthermore, the objective quality metrics PCQM, PSNR D1, PSNR D2, PSNR Y and PSNR YUV were computed, and compared with the subjective evaluation results. This study revealed that the deep learning solutions outperformed G-PCC but were still below the performance of V-PCC regarding color representation. PCQM showed the best performance in predicting the compression quality.

Common representations of light fields use four-dimensional data structures, where a given pixel is closely related not only to its spatial neighbours within the same view, but also to its angular neighbours, co-located in adjacent views. Such structure presents increased redundancy between pixels, when compared with regular single-view images. Then, these redundancies are exploited to obtain compressed representations of the light field, using prediction algorithms specifically tailored to estimate pixel values based on both spatial and angular references. This paper proposes new encoding schemes which take advantage of the four-dimensional light field data structures to improve the coding performance of Minimum Rate Predictors. The proposed methods expand previous research on lossless coding beyond the current state-of-the-art. The experimental results, obtained using both traditional datasets and others more challenging, show bit-rate savings no smaller than 10%, when compared with existing methods for lossless light field compression.

In this work we compare two different options to perform on-line subjective quality assessment experiments in the context of the Call for Evidence on JPEG Pleno Point Cloud Coding. A deep-learning based point cloud codec submitted to the Call was tested against current MPEG point cloud compression methods. The first option is based on participants downloading the entire set of stimuli and running a set of scripts in MATLAB to perform the experiment. The second option involves the participants accessing a server on the web and viewing and judging the stimuli using a web browser. Quality scores compiled using both methods were compared showing strong correlation. A second analysis compared the quality scores with those obtained in a prior laboratory-based study using higher resolution screens. The entire study also brought to light each option’s unique advantages and disadvantages that make each one better suited to specific types of subjective evaluation contexts and situations.

In this work, we propose a spatially adaptive HEVC intra mode pre-selection for equirectangular (ERP) 360 video coding. The proposed technique exploits the spatial characteristics of 360 video in the ERP projection to reduce the complexity of intra prediction mode selection. The number of intra modes evaluated in Rate-Distortion Optimization is reduced based on a score technique that is adaptive to the frame region being encoded. Results show that the proposed technique achieves a complexity reduction of 16.5% with low coding efficiency penalties.

This paper presents a quality evaluation study of point cloud codecs that have been recently standardised by the MPEG committee. In particular, a subjective experiment to assess their performance in terms of bitrate against visual quality is designed and realized in four independent laboratories. The experimental setup of each laboratory varies; yet, the obtained subjective scores exhibit high inter laboratory correlation, confirming that the adopted assessment protocol is robust to equipment selection and viewing conditions, ensuring reliability and facilitating repeatability. Our study confirms the superior compression performance of the MPEG V-PCC, when compared to MPEG G-PCC, in the case of static contents. Finally, results from a benchmark of the most popular objective quality metrics using the obtained subjective scores as ground truth, reveal that the point2plane with mean square error is the most accurate quality predictor, closely followed by the point2point also using mean square error as distance measure.

Efficient light field encoders take advantage of the inherent 4D data structures to achieve high compression performance. This is accomplished by exploiting the redundancy of co-located pixels in different sub-aperture images (SAIs) through prediction and/or transform schemes to find a m ore compact representation of the signal. However, in image regions with higher disparity between SAIs, such scheme's performance tends to decrease, thus reducing the compression efficiency. This paper introduces a reversible pre-processing algorithm for disparity compensation that operates on the SAI domain of light field data. The proposed method contributes to improve the transform efficiency of the encoder, since the disparity-compensated data presents higher correlation between co-located image blocks. The experimental results show significant improvements in the compression performance of 4D light fields, achieving Bjontegaard delta rate gains of about 44% on average for MuLE codec using the 4D discrete cosine transform, when encoding High Density Camera Arrays (HDCA) light field images.

This paper presents a method to improve the lossless compression efficiency of light field encoding based on Minimum Rate Predictors (MRP). The proposed method relies on the use of multiple references, either micro-images or sub-aperture images, to provide a richer set of correlated pixels for prediction. The results show better compression ratios than conventional versions of MRP, for both representation formats (micro-image and sub-aperture image arrays), achieving gains ranging from 16.9% to 21.2%. Furthermore, it is also shown that the proposed method consistently outperforms the state-of-the-art lossless encoders HEVC and JPEG-LS.

360-degrees videos represent a whole sphere and enable the user to feel as if he is inside the scene. These videos demand more data than conventional videos to be represented, therefore they also must be compressed to be handled properly. However, current video coding standards only process rectangular videos, thus 360 videos must be represented in a flat fashion to be encoded. There are several projections to perform this and the currently most used one is the equirectangular projection (ERP), which transforms each parallel from the sphere into a row of the rectangle, resulting in a faithful representation of the equatorial area, and a stretched representation of the polar regions. This stretching in the polar regions tends to impact the behavior of intra-frame prediction, which is used to exploit the spatial redundancies in each frame. Therefore, this paper proposes FastIntra360 to accelerate the encoding of 360 videos. FastIntra360 is implemented in HEVC video coding standard [1], which is a recently established standard and poses high computational demand. During the development of FastIntra360, a set of videos were encoded and the behavior of the intra-prediction throughout the frame was extracted. Then, a statistical analysis was conducted over such data and it concluded that when encoding the polar regions of the frame, the prediction modes which exploit horizontal directions are selected more frequently than the remaining modes, whereas in the center of the frame all prediction modes present similar occurrence rates. FastIntra360 exploits this behavior to reduce the number of prediction modes evaluated in different regions of the frame to accelerate the encoding. FastIntra360 is developed in two variants: one considering three bands and other considering five bands, where each band is a horizontal stripe of the frame. Each band divides the frame samples into three or five stripes and performs the statistical analysis over these stripes individually. Both implementations were evaluated and compared against the HEVC Test Model version 16.16 (HM-16.16) according to time reduction and coding efficiency (considering BD-BR), where BD-BR represents the bitrate increase of the proposed technique. Experimental results showed that both implementations present good performance, reaching up to 16.5% complexity reduction with negligible BD-BR, that is, they present considerable complexity reduction whereas posing no harm to the video quality.

Recently stakeholders in the area of multimedia representation and transmission have been looking at plenoptic technologies to improve immersive experience. Among these technologies, point clouds denote a volumetric information representation format with important applications in the entertainment, automotive and geographical mapping industries. There is some consensus that state-of-the-art solutions for efficient storage and communication of point clouds are far from satisfactory. This paper describes a study on point cloud quality evaluation, conducted in the context of JPEG Pleno to help define the test conditions of future compression proposals. A heterogeneous set of static point clouds in terms of number of points, geometric structure and represented scenarios were selected and compressed using octree-pruning and a projection-based method, with three different levels of degradation. The models were comprised of both geometrical and color information and were displayed using point sizes large enough to ensure observation of watertight surfaces. The stimuli under assessment were presented to the observers on 2D displays as animations, after defining suitable camera paths to enable visualization of the models in their entirety and realistic consumption. The experiments were carried out in three different laboratories and the subjective scores were used in a series of correlation studies to benchmark objective quality metrics and assess inter-laboratory consistency.

Despite the several speedup methods proposed in the literature, the computational complexity of High Efficiency Video Coding (HEVC) video encoding is still a problem. This paper proposes a fast coding unit (CU) partition decision for use in HEVC encoders based on support vector machine (SVM)-trained offline. The SVM classifiers, features, and training procedures are described in detail, and a justification for the use of SVMs is provided. The trained classifiers are incorporated into a modified reference encoder in the form of a fast CU partition decision algorithm, which decides if the exhaustive search for the best partition is continued or terminated prematurely. Using the proposed method, an average complexity reduction of 48% is achieved with a 0.48% Bjontegaard-Delta bitrate (BD-BR) loss using the random access coding configuration, 44% reduction with a 0.62% BD-BR loss for the Low Delay B, and a 41% reduction with a 0.6% BD-BR loss for the Low Delay P configuration. We also tested our approach under constant bitrate conditions, achieving a 47% reduction in encoding time with a 1.11% loss in the BD-BR. In addition, a decision threshold adaptation is also proposed to allow adjusting the rate-distortion/complexity trade-off of our solution. With this approach, the computational complexity reduction can be varied from 34.9% (with a 0.13% loss in the BD-BR) up to 52.4% (with a 1.11% BD-BR loss) using the random access configuration. Compared with the state-of-the-art solutions, our decision scheme outperforms the related works in terms of combined rate distortion and complexity.

With the exponential growth of digital data available in ophthalmology the implementation of effective Computer-Aided Diagnosis Systems can make clinical diagnosis more accurate and accessible and therefore improve the standard of care. We explore a multimodal electronic health record dataset (n = 2348 cases and n = 2348 controls) and propose a new classifier model based on Random Forrest Classifiers for recommendation of an ophthalmic procedure (intravitreal injection of bevacizumab). The dataset comprises structured demographic data, unstructured textual annotations, and clinical images (optical coherence tomography, slit lamp photography and scanning laser ophthalmoscopy). Textual annotations were processed and encoded using a standardized medical ontology nomenclature (SNOMED-CT) to enable higher level modeling and additional meta-features were engineered in the ontology domain. Raster image data were used to train several convolutional neural networks (CNN) which were used as feature generators. Each subset of features was used for clinical event modeling. To the best of our knowledge this is the first work to be published proposing the integration of clinical textual, ontological and image data information for clinical event prediction in ophthalmology. An unoptimized implementation of the model proposed achieves a prediction accuracy of 87.38% when all data modalities are combined. When using demographic, ontological and image features in isolation performances are 72.98%, 84.37% and 78.77% respectively

This article proposes a fast Coding Unit (CU) partition decision for use in HEVC encoders based on Decision Tree classifiers. The trees are employed in a modified low-complexity encoder that implements a fast CU partition decision algorithm. Using the proposed method, an average complexity reduction of 47.8% is achieved with a Bjontegaard Delta bitrate (BD-BR) loss of 0.24% in the Random Access coding configuration, and a 42.8% complexity reduction with a 0.19% BD-BR loss in the Low Delay B configuration. A decision threshold analysis is also presented to assess the rate-distortion-complexity trade-off of the proposed method at different complexity points, varying the complexity reduction from 28% (with a 0.04% loss in BD-BR) up to 60% (with a 3.6% BD-BR loss) using the Random Access configuration. A comparison with related works shows that the proposed method outperforms competing solutions in terms of both rate-distortion efficiency and complexity reduction.

Point clouds are one of the most promising technologies for 3D content representation. In this paper, we describe a study on quality assessment of point clouds, degraded by octree-based compression on different levels. The test contents were displayed using Screened Poisson surface reconstruction, without including any textural information, and they were rated by subjects in a passive way, using a 2D image sequence. Subjective evaluations were performed in five independent laboratories in different countries, with the inter-laboratory correlation analysis showing no statistical differences, despite the different equipment employed. Benchmarking results reveal that the state-of-the-art point cloud objective metrics are not able to accurately predict the expected visual quality of such test contents. Moreover, the subjective scores collected from this experiment were found to be poorly correlated with subjective scores obtained from another test involving visualization of raw point clouds. These results suggest the need for further investigations on adequate point cloud representations and objective Quality assessment tools.

This article describes a fast transrating solution for HEVC based on classification and machine learning techniques. Two classifiers are trained to predict the range of CTU quadtree depths that will be searched to find the best CTU partitioning. Three approaches are proposed for reducing the number of features used by the classifiers, two based on feature selection, and one based on feature transformation using autoencoders. A full transrating framework based on x265 is built for model training and evaluation. Experimental results using the x265 encoder show that an average 41.81% computational complexity reduction can be achieved at the cost of a tolerable 0.29% Bjontegaard-Delta bitrate, outperforming competing methods.

In recent years, 3D point clouds have gained more attention with the possibility of applications such as virtual reality, autonomous vehicles and 3D mapping of historical artifacts, among others. However, raw point clouds generate very large amounts of data. Thus, compression is essential to enable emerging 3D systems for communication and storage. This paper presents an inter prediction technique based on the ICP algorithm and variable-size macroblocks that can be used to significantly reduce the number of bits required to represent a point cloud video sequence by inter coding the geometry information. Since consecutive frames in dynamic point cloud sequences are not guaranteed to fill the exact same 3D volume, a spatial alignment step before motion estimation is required to increase the likelihood of good matchings and thus generate a high number of inter-coded macroblocks. A decision step is also included in order to select the most favorable coding mode: intra-coding, inter-coding or inter-coding with macroblock subdivision. The proposed technique was tested in the PCC MPEG reference software and four MPEG test sequences, obtaining average bitrate reductions of about 8% with PSNR gains up to 1dB.

The emergence of 3D video technologies and their foreseeable applications in Internet Protocol Television (IPTV) and video delivery systems using encoded 3D video contents over packet networks raises the question of how to manage the Quality of Experience (QoE) across diverse lossy channels. Of particular importance for QoE management is the problem of measuring the impact of data losses in packetized 3D video information and how it affects the quality experienced by end users, when the content is rendered and presented at their premises. Previous work by the authors showed that it is possible to model the perceived quality degradations through the use of artificial neural networks receiving as inputs several parameters describing the packet loss events. The first stage of such model was specifically developed for packetized 3D video in texture-plus-depth format where only the depth information was prone to transmission errors. This article presents an extension of the previous model by including the effect of texture information losses along with other specific aspects associated with the dual nature of this type of data loss. The validity of the model is verified through the use of extensive simulations and comparisons between real and estimated values of a recently proposed 3D video quality measure.

This paper describes a highly efficient method for lossless compression of volumetric sets of medical images, such as CTs or MRIs. The proposed method, referred to as 3-D-MRP, is based on the principle of minimum rate predictors (MRPs), which is one of the state-of-the-art lossless compression technologies presented in the data compression literature. The main features of the proposed method include the use of 3-D predictors, 3-D-block octree partitioning and classification, volume-based optimization, and support for 16-b-depth images. Experimental results demonstrate the efficiency of the 3-D-MRP algorithm for the compression of volumetric sets of medical images, achieving gains above 15% and 12% for 8- and 16-bit-depth contents, respectively, when compared with JPEG-LS, JPEG2000, CALIC, and HEVC, as well as other proposals based on the MRP algorithm.

High Efficiency Video Coding (HEVC) is gradually replacing its predecessor, the H.264/AVC standard, as the state-of-the-art technology for video compression. However, H.264/AVC has dominated the market for over a decade, so that there is an enormous amount of legacy content that must be migrated. This paper proposes a fast transcoder based on an extensive data mining process on H.264/AVC decoding attributes. The data mining allowed identifying relevant information from the H.264/AVC decoding process, which was conveyed to the C4.5 machine learning algorithm to build a set of decision trees that simplify the complex Coding Unit (CU) size decision in HEVC. Experimental results have shown an average reduction of 44% in the transcoding time, with a small bit rate increase of 1.67%. These results outperform any previous works available in the literature.

Medical imaging technology has become essential for the improvement of medical practice. This led to advances in the technology, namely in image sampling resolutions, pixel bit-depth and inter slice resolution. Additionally, common use of medical images, the life expectancy of patients and legal restrictions led to increasing storage costs. Therefore, efficient compression of medical image data is in high demand, for archiving and transmission. In this work we propose to improve the compression efficiency of the Minimum Rate Predictors lossless encoder, by adding bi-directional prediction support and a histogram packing technique. The results show that the proposed method presents a higher compression efficiency than state-of-the-art HEVC encoder. The compression efficiency is improved by 20%, on average, when compared to HEVC and by 46.1% when compared with the original MRP algorithm.

Several different methods have been investigated in recent years, aiming at computational complexity reduction and scaling of High Efficiency Video Coding (HEVC) software implementations. However, maintaining the encoding time per frame or group of pictures (GOPs) below an adjustable upper bound is still an open research issue. A solution for this problem is devised in this paper based on a set of Pareto-efficient encoding configurations, identified through rate-distortion-complexity analysis. The proposed method combines a medium-granularity encoding time control with a fine-granularity encoding time control to accurately limit the HEVC encoding time below a predefined target for each GOP. It is shown that the encoding time can be kept below a desired target for a wide range of encoding time reductions, e.g., up to 90% in comparison with the original encoder. The results also show that compression efficiency loss (Bjøntegaard delta-rate) varies from negligible (0.16%) to moderate (9.83%) in the extreme case of 90% computational complexity reduction.

Efficient computation of visual saliency regions has been a research problem in the recent past, but in the case of 3D content no definite solutions exist. This paper presents a computational method to determine saliency regions in 3D video, based on fusion of three feature maps containing perceptually relevant information from spatial, temporal and depth dimensions. The proposed method follows a bottom-up approach to predict the 3D regions where observers tend to hold their gaze for longer periods. Fusion of the feature maps is combined with a center-bias weighting function to determine 3D visual saliency map. For validation and performance evaluation, a publicly available database of 3D video sequences and corresponding fixation density maps was used as ground-truth. The experimental results show that the proposed method achieves better performance than other state-of-art models.

The increasing availability of 3D video systems and applications has attracted more consumers for 3D viewing experiences and, consequently, the demand for storage and transmission of 3D video content is growing. An interesting alternative for this need is the transmission of 3D video based on the Multiview Video plus Depth (MVD) format. The upcoming 3D High Efficiency Video Coding (3D-HEVC) standard will adopt this format, which associates a depth map to each texture frame. This paper presents a fast mode decision algorithm, which analyses the texture frames and depth maps to detect the edge orientation of the prediction units (PUs), optimizing the intra prediction process and reducing the 3D-HEVC computational complexity. The experimental results show that the proposed algorithm achieves an average processing time reduction of 26.2%, with a small degradation in encoding efficiency (BD-rate increase of 0.3% on average).

The improved compression efficiency of High Efficiency Video Coding (HEVC) comes with large increases in computational complexity, which has been lately dealt by researchers with complexity reduction and scaling methods. However, encoding time control at frame or Group of Pictures (GOP) level is still an open issue that must be investigated. In this work, a Rate-Distortion-Complexity analysis is performed upon a set of configurations that have been created based on findings and techniques of previous works. The proposed control system uses the best 27 configurations to adjust the encoding time per GOP and yields encoding time reductions of up to 84.5% with an average difference between target and encoding times of 4.1%.

The High Efficiency Video Coding standard provides improved compression ratio in comparison with its predecessors at the cost of large increases in the encoding computational complexity. An important share of this increase is due to the new flexible partitioning structures, namely the coding trees, the prediction units, and the residual quadtrees, with the best configurations decided through an exhaustive rate-distortion optimization (RDO) process. In this paper, we propose a set of procedures for deciding whether the partition structure optimization algorithm should be terminated early or run to the end of an exhaustive search for the best configuration. The proposed schemes are based on decision trees obtained through data mining techniques. By extracting intermediate data, such as encoding variables from a training set of video sequences, three sets of decision trees are built and implemented to avoid running the RDO algorithm to its full extent. When separately implemented, these schemes achieve average computational complexity reductions (CCRs) of up to 50% at a negligible cost of 0.56% in terms of Bjontegaard Delta (BD) rate increase. When the schemes are jointly implemented, an average CCR of up to 65% is achieved, with a small BD-rate increase of 1.36%. Extensive experiments and comparisons with similar works demonstrate that the proposed early termination schemes achieve the best rate-distortion-complexity tradeoffs among all the compared works.

A no-reference (NR) method based on an artificial neural network (ANN) approach is proposed in this paper to estimate the objective quality of video-plus-depth streams subject to packet loss in depth data. A novel aspect of this method is the use of information only taken from packet headers, up to the network abstraction layer (NAL), requiring a very low complexity parsing of the compressed video streams. A maximum of seven packet-layer parameters were found to be enough to provide accurate objective quality estimates given by the structural similarity index (SSIM). The accuracy of the quality estimates, evaluated by comparison with the actual SSIM quality scores, is shown to be sufficiently high (e.g., Pearson Linear Correlation Coefficient over 0.92) for lightweight implementations of 3D video quality monitors at end-user receivers and also at network nodes.

The flexible encoding structures of High Efficiency Video Coding (HEVC) are the main responsible for the improvements of the standard in terms of compression efficiency in comparison to its predecessors. However, the flexibility provided by these structures is accompanied by high levels of computational complexity, since more options are considered in a Rate-Distortion (R-D) optimization scheme. In this paper, we propose a four-step early-termination method, which decides whether the inter mode decision should be halted without testing all possibilities. The method employs a set of decision trees, which are trained offline once, using information from unconstrained HEVC encoding runs. The resulting trees present a mode decision accuracy ranging from 97.6% to 99.4% with a negligible computational overhead. The method is capable of achieving an average computational complexity decrease of 49% at the cost of a very small Bjontegaard Delta (BD)-rate increase (0.58%).

Three dimensional (3D) video technology, systems and applications such as 3D television and freeviewpoint television (FTV) broadcasts require efficient encoding of video information. To fill that need a 3D video extension of High Efficiency Video Coding standard, called 3D-HEVC, is being developed. This extension is based on Multiview Video plus Depth (MVD) format, which associates a depth information to each texture frame of each view. This paper presents a method to accelerate the intra coding of these depth maps to reduce the 3D-HEVC computational complexity. The proposed algorithm exploits the edge orientation of the depth blocks to reduce the number of modes to be evaluated in the intra mode decision. In addition, the correlation between the Planar mode choice and the most probable modes (MPMs) selected is also exploited, to accelerate the depth intra coding. The experimental results show that the proposed algorithm achieves an average complexity reduction of 15% on depth information encoding, with a small degradation in encoding efficiency (BD-rate increase of 0.16% on average).

Eye diseases such as diabetic retinopathy are sometimes treated with recourse to laser based cauterization. The marks left by the action of the laser on the surface of the retina can cause misbehaviors of automated retinal diagnostic systems. It is therefore highly desirable to be able to detect the presence of laser marks left behind by such treatments and use that information in the first steps of automatic retinal diagnostics to prevent further unnecessary processing. In this paper we present an attempt to detect the presence of laser marks using an automated detection algorithm and present some preliminary results on its performance.

The emerging HEVC standard introduces a number of tools which increase compression efficiency in comparison to its predecessors at the cost of greater computational complexity. This paper proposes a complexity control method for HEVC encoders based on dynamic adjustment of the newly proposed coding tree structures. The method improves a previous solution by adopting a strategy that takes into consideration both spatial and temporal correlation in order to decide the maximum coding tree depth allowed for each coding tree block. Complexity control capability is increased in comparison to a previous work, while compression losses are decreased by 70%. Experimental results show that the encoder computational complexity can be downscaled to 60% with an average bit rate increase around 1.3% and a PSNR decrease under 0.07 dB.

The High Efficiency Video Coding standard shows improved compression efficiency in comparison to previous standards at the cost of higher computational complexity. In this paper, a complexity scalability method for HEVC encoders based on the dynamic adjustment of the number of constrained coding treeblocks is proposed. The method limits the Prediction Unit shapes and the maximum tree depth used in each Coding Treeblock in order to decrease the number of evaluations performed in the Rate-Distortion Optimization process. The encoder is capable of trading off computational complexity and compression efficiency while still maintaining the encoding time per Group of Pictures (GOP) under a pre-defined target. The encoding complexity can be decreased in up to 60% when compared to the original encoder at the cost of small Bjontegaard Delta (BD)-rate increases.

The new High Efficiency Video Coding (HEVC) standard is achieving higher encoding efficiency when compared to its predecessors such as H.264/AVC. One of the factors responsible to this improvement is the intra prediction method, which introduces a larger number of prediction directions resulting in an enhanced rate-distortion (RD) performance at the cost of a higher computational complexity. This paper proposes an algorithm to accelerate the intra mode decision, reducing the complexity of intra coding. The acceleration procedure takes into account the edge direction information and explores the correlation of intra modes across levels of the HEVC hierarchical tree structure. Experimental results show that the proposed algorithm provides a decrease of up to 40.82% in the HEVC intra prediction processing time, with a small degradation in encoding efficiency (BD-PSNR loss of 0.1 dB on average).

The recently introduced quadtree coding structures used in HEVC increase compression efficiency in comparison to previous standards at the cost of higher computational complexity levels. This paper proposes an evolution of a complexity control method for HEVC encoders based on the dynamic adjustment of these structures' maximum depth. The new method improves the previous solution by adopting a new control strategy and compensating the motion effect on a maximum tree depth map which is central to the complexity control strategy. The proposed method is capable of performing a more accurate complexity control than our previous strategy while still reducing compression efficiency losses in terms of image quality and bit rate.

The emerging HEVC standard introduces several techniques which increase compression efficiency in comparison to its predecessors. However, such advances are accompanied by increases in computational complexity, limiting the encoder use in computational or power-constrained devices. This paper proposes a novel complexity control method for the future HEVC encoders based on a dynamic adjustment of the newly proposed coding tree structures. The relationship between coding tree depths and the encoding complexity is explored to selectively constrain encoding possibilities in order to not exceed a predefined complexity target. Experimental results show that the encoder computational complexity can be downscaled to 60% with a bit rate increase under 3.5% and a PSNR decrease under 0.1 dB.

This paper presents a performance evaluation study of coding efficiency versus computational complexity for the forthcoming High Efficiency Video Coding (HEVC) standard. A thorough experimental investigation was carried out to identify the tools that most affect the encoding efficiency and computational complexity of the HEVC encoder. A set of 16 different encoding configurations was created to investigate the impact of each tool, varying the encoding parameter set and comparing the results with a baseline encoder. This paper shows that, even though the computational complexity increases monotonically from the baseline to the most complex configuration, the encoding efficiency saturates at some point. Moreover, the results of this paper provide relevant information for implementation of complexity-constrained encoders by taking into account the tradeoff between complexity and coding efficiency. It is shown that low-complexity encoding configurations, defined by careful selection of coding tools, achieve coding efficiency comparable to that of high-complexity configurations.

Block based full search motion estimation is an expensive technique both in terms of computation and motion side information. An adaptive method based on vector quantization techniques with a dynamically updated codebook is introduced which provides a reduction of the complexity of the motion estimation process while keeping the side information low.