A Motion Estimation based Algorithm for Encoding Time Reduction in HEVC

High Efficiency Video Coding (HEVC) is a video compression standard that offers 50% more efficiency at the expense of high encoding time contrasted with the H.264 Advanced Video Coding (AVC) standard. The encoding time must be reduced to satisfy the needs of real-time applications. This paper has proposed the Multi- Level Resolution Vertical Subsampling (MLRVS) algorithm to reduce the encoding time. The vertical subsampling minimizes the number of Sum of Absolute Difference (SAD) computations during the motion estimation process. The complexity reduction algorithm is also used for fast coding the coefficients of the quantised block using a flag decision. Two distinct search patterns are suggested: New Cross Diamond Diamond (NCDD) and New Cross Diamond Hexagonal (NCDH) search patterns, which reduce the time needed to locate the motion vectors. In this paper, the MLRVS algorithm with NCDD and MLRVS algorithm with NCDH search patterns are simulated separately and analyzed. The results show that the encoding time of the encoder is decreased by 55% with MLRVS algorithm using NCDD search pattern and 56% with MLRVS using NCDH search pattern compared to HM16.5 with Test Zone (TZ) search algorithm. These results are achieved with a slight increase in bit rate and negligible deterioration in output video quality

Machine Learning based Efficient QT-MTT Partitioning Scheme for VVC Intra Encoders

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

Towards Hybrid-Optimization Video Coding

Video coding is a mathematical optimization problem of rate and distortion essentially. To solve this complex optimization problem, two popular video coding frameworks have been developed: block-based hybrid video coding and end-to-end learned video coding. If we rethink video coding from the perspective of optimization, we find that the existing two frameworks represent two directions of optimization solutions. Block-based hybrid coding represents the discrete optimization solution because those irrelevant coding modes are discrete in mathematics. It searches for the best one among multiple starting points (i.e. modes). However, the search is not efficient enough. On the other hand, end-to-end learned coding represents the continuous optimization solution because the gradient descent is based on a continuous function. It optimizes a group of model parameters efficiently by the numerical algorithm. However, limited by only one starting point, it is easy to fall into the local optimum. To better solve the optimization problem, we propose to regard video coding as a hybrid of the discrete and continuous optimization problem, and use both search and numerical algorithm to solve it. Our idea is to provide multiple discrete starting points in the global space and optimize the local optimum around each point by numerical algorithm efficiently. Finally, we search for the global optimum among those local optimums. Guided by the hybrid optimization idea, we design a hybrid optimization video coding framework, which is built on continuous deep networks entirely and also contains some discrete modes. We conduct a comprehensive set of experiments. Compared to the continuous optimization framework, our method outperforms pure learned video coding methods. Meanwhile, compared to the discrete optimization framework, our method achieves comparable performance to HEVC reference software HM16.10 in PSNR

Fast mode decision in the HEVC Video coding standard by exploiting region with dominated motion and saliency features

The emerging High Efficiency Video Coding (HEVC) standard introduces a number of innovative and powerful coding tools to acquire better compression efficiency compared to its predecessor H.264. The encoding time complexities have also increased multiple times that is not suitable for realtime video coding applications. To address this limitation, this paper employs a novel coding strategy to reduce the time complexity in HEVC encoder by efficient selection of appropriate block-partitioning modes based on human visual features (HVF). The HVF in the proposed technique comprise with human visual attention modelling-based saliency feature and phase correlation-based motion features. The features are innovatively combined through a fusion process by developing a content-based adaptive weighted cost function to determine the region with dominated motion/saliency (RDMS)- based binary pattern for the current block. The generated binary pattern is then compared with a codebook of predefined binary pattern templates aligned to the HEVC recommended block-paritioning to estimate a subset of inter-prediction modes. Without exhaustive exploration of all modes available in the HEVC standard, only the selected subset of modes are motion estimated and motion compensated for a particular coding unit. The experimental evaluation reveals that the proposed technique notably down-scales the average computational time of the latest HEVC reference encoder by 34% while providing similar rate-distortion (RD) performance for a wide range of video sequences

Efficient video coding using visual sensitive information for HEVC coding standard

The latest high efficiency video coding (HEVC) standard introduces a large number of inter-mode block partitioning modes. The HEVC reference test model (HM) uses partially exhaustive tree-structured mode selection, which still explores a large number of prediction unit (PU) modes for a coding unit (CU). This impacts on encoding time rise which deprives a number of electronic devices having limited processing resources to use various features of HEVC. By analyzing the homogeneity, residual, and different statistical correlation among modes, many researchers speed-up the encoding process through the number of PU mode reduction. However, these approaches could not demonstrate the similar rate-distortion (RD) performance with the HM due to their dependency on existing Lagrangian cost function (LCF) within the HEVC framework. In this paper, to avoid the complete dependency on LCF in the initial phase, we exploit visual sensitive foreground motion and spatial salient metric (FMSSM) in a block. To capture its motion and saliency features, we use the dynamic background and visual saliency modeling, respectively. According to the FMSSM values, a subset of PU modes is then explored for encoding the CU. This preprocessing phase is independent from the existing LCF. As the proposed coding technique further reduces the number of PU modes using two simple criteria (i.e., motion and saliency), it outperforms the HM in terms of encoding time reduction. As it also encodes the uncovered and static background areas using the dynamic background frame as a substituted reference frame, it does not sacrifice quality. Tested results reveal that the proposed method achieves 32% average encoding time reduction of the HM without any quality loss for a wide range of videos

Low complexity in-loop perceptual video coding

The tradition of broadcast video is today complemented with user generated content, as portable devices support video coding. Similarly, computing is becoming ubiquitous, where Internet of Things (IoT) incorporate heterogeneous networks to communicate with personal and/or infrastructure devices. Irrespective, the emphasises is on bandwidth and processor efficiencies, meaning increasing the signalling options in video encoding. Consequently, assessment for pixel differences applies uniform cost to be processor efficient, in contrast the Human Visual System (HVS) has non-uniform sensitivity based upon lighting, edges and textures. Existing perceptual assessments, are natively incompatible and processor demanding, making perceptual video coding (PVC) unsuitable for these environments. This research allows existing perceptual assessment at the native level using low complexity techniques, before producing new pixel-base image quality assessments (IQAs). To manage these IQAs a framework was developed and implemented in the high efficiency video coding (HEVC) encoder. This resulted in bit-redistribution, where greater bits and smaller partitioning were allocated to perceptually significant regions. Using a HEVC optimised processor the timing increase was < +4% and < +6% for video streaming and recording applications respectively, 1/3 of an existing low complexity PVC solution. Future work should be directed towards perceptual quantisation which offers the potential for perceptual coding gain

Quality Evaluations and algorithmic Improvement of the next Generation Video Coding - HEVC

The increased processing power and screen sizes of mobile devices has made it desirable to watch multimedia presentations on the go. On such devices the data network bandwidth is usually the limiting factor, which imposes a tradeoff between quality and resolution on the presented content. A new video compression system called High Efficiency Video Coding (HEVC) is currently under development. The vision of HEVC is to create a compression system that achieves the same quality at half the bit rate compared to the existing H.264/AVC standard [2].The goal of this thesis is to investigate how HEVC performs compared to H.264/AVC using mobile platforms and sport content as the scenario. The subjective test was conducted on an Apple iPad. It indicated that HEVC has a clear gain in compression compared to H.264/AVC. On average at a resolution of 640x368, HEVC achieved a good quality rating at approximately 550 kilobit per second while H.264/AVC did almost reach this quality at 1000 kilobit per second. However, it was shown that subjective quality gain varied over content.The objective measurements showed an overall reduction in bit rate of 32% forthe luma component. However, the reduction of bit rate was highly variable over content and resolution. A high correlation between the subjective and objective measurements was found, which indicates that it was almost a linear relationship between the reported subjective and objective results.In addition, a proposed deblocking filter was implemented. The filter applies a new filter function of the luma samples and performs line based filtering decision. On average the reduction in bit rate was reported to be 0.4%, with a maximum reduction of 0.8% for the luma component. The decoding time relative to the second version of the HEVC test model was reported to be 1.5% higher. This is most likely due to the line based filtering decision. The general impression of HEVC is that it has the ability to reach the stated vision, and perhaps even surpass, when finalized

Novi algoritam za kompresiju seizmičkih podataka velike amplitudske rezolucije

Renewable sources cannot meet energy demand of a growing global market. Therefore, it is expected that oil & gas will remain a substantial sources of energy in a coming years. To find a new oil & gas deposits that would satisfy growing global energy demands, significant efforts are constantly involved in finding ways to increase efficiency of a seismic surveys. It is commonly considered that, in an initial phase of exploration and production of a new fields, high-resolution and high-quality images of the subsurface are of the great importance. As one part in the seismic data processing chain, efficient managing and delivering of a large data sets, that are vastly produced by the industry during seismic surveys, becomes extremely important in order to facilitate further seismic data processing and interpretation. In this respect, efficiency to a large extent relies on the efficiency of the compression scheme, which is often required to enable faster transfer and access to data, as well as efficient data storage. Motivated by the superior performance of High Efficiency Video Coding (HEVC), and driven by the rapid growth in data volume produced by seismic surveys, this work explores a 32 bits per pixel (b/p) extension of the HEVC codec for compression of seismic data. It is proposed to reassemble seismic slices in a format that corresponds to video signal and benefit from the coding gain achieved by HEVC inter mode, besides the possible advantages of the (still image) HEVC intra mode. To this end, this work modifies almost all components of the original HEVC codec to cater for high bit-depth coding of seismic data: Lagrange multiplier used in optimization of the coding parameters has been adapted to the new data statistics, core transform and quantization have been reimplemented to handle the increased bit-depth range, and modified adaptive binary arithmetic coder has been employed for efficient entropy coding. In addition, optimized block selection, reduced intra prediction modes, and flexible motion estimation are tested to adapt to the structure of seismic data. Even though the new codec after implementation of the proposed modifications goes beyond the standardized HEVC, it still maintains a generic HEVC structure, and it is developed under the general HEVC framework. There is no similar work in the field of the seismic data compression that uses the HEVC as a base codec setting. Thus, a specific codec design has been tailored which, when compared to the JPEG-XR and commercial wavelet-based codec, significantly improves the peak-signal-tonoise- ratio (PSNR) vs. compression ratio performance for 32 b/p seismic data. Depending on a proposed configurations, PSNR gain goes from 3.39 dB up to 9.48 dB. Also, relying on the specific characteristics of seismic data, an optimized encoder is proposed in this work. It reduces encoding time by 67.17% for All-I configuration on trace image dataset, and 67.39% for All-I, 97.96% for P2-configuration and 98.64% for B-configuration on 3D wavefield dataset, with negligible coding performance losses. As a side contribution of this work, HEVC is analyzed within all of its functional units, so that the presented work itself can serve as a specific overview of methods incorporated into the standard