Pallab Kanti Podder

Fast Intermode Selection for HEVC Video Coding Using Phase Correlation

The recent High Efficiency Video Coding (HEVC) Standard demonstrates higher rate-distortion (RD) performance compared to its predecessor H.264/AVC using different new tools especially larger and asymmetric inter-mode variable size motion estimation and compensation. This requires more than 4 times computational time compared to H.264/AVC. As a result it has always been a big concern for the researchers to reduce the amount of time while maintaining the standard quality of the video. The reduction of computational time by smart selection of the appropriate modes in HEVC is our motivation. To accomplish this task in this paper, we use phase correlation to approximate the motion information between current and reference blocks by comparing with a number of different binary pattern templates and then select a subset of motion estimation modes without exhaustively exploring all possible modes. The experimental results exhibit that the proposed HEVC-PC (HEVC with Phase Correlation) scheme outperforms the standard HEVC scheme in terms of computational time while preserving-the same quality of the video sequences. More specifically, around 40% encoding time is reduced compared to the exhaustive mode selection in HEVC.

A novel motion classification based intermode selection strategy for HEVC performance improvement

High Efficiency Video Coding (HEVC) standard adopts several new approaches to achieve higher coding efficiency (approximately 50% bit-rate reduction) compared to its predecessor H.264/AVC with same perceptual image quality. Huge computational time has also increased due to the algorithmic complexity of HEVC compared to H.264/AVC. However, it is really a demanding task to reduce the encoding time while preserving the similar quality of the video sequences. In this paper, we propose a novel efficient intermode selection technique and incorporate into HEVC framework to predict motion estimation and motion compensation modes between current and reference blocks and perform faster inter mode selection based on three dissimilar motion types in divergent video sequences. Instead of exploring and traversing all the modes exhaustively, we merely select a subset of candidate modes and the final mode from the selected subset is determined based on their lowest Lagrangian cost function. The experimental results reveal that average encoding time can be downscaled by 40% with similar rate-distortion performance compared to the exhaustive mode selection strategy in HEVC.

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 coding strategy for HEVC performance improvement by exploiting motion features

The striking feature of High Efficiency Video Coding (HEVC) Standard is emphasized by 50% bit-rate reduction compared to its predecessor H.264/AVC while keeping the same perceptual image quality. The time complexity - a congenital issue of HEVC has also increased to intensify the compression ratio. However, it is really a demanding task for the researchers to reduce the encoding time while preserving expected quality of the video sequences. Our contribution is to trim down the computational time by efficient selection of appropriate block-partitioning modes in HEVC using motion features based on phase-correlation. In this paper, we use phase-correlation between current and reference blocks to extract three motion features and combine them to determine binary motion pattern of the current block. The motion pattern is then matched against a codebook of predefined pattern templates to determine a subset of the inter-modes. Only the selected modes are exhaustively motion estimated and compensated for a coding unit. The experimental outcomes demonstrate that the average computational time can be down scaled by 30% of the HEVC while providing improved rate-distortion performance.

Efficient HEVC Scheme Using Motion Type Categorization

High Efficiency Video Coding (HEVC) standard introduces a number of innovative tools which can reduce approximately 50% bit-rate compared to its predecessor H.264/AVC at the same perceptual video quality whereas the computational time has increased multiple times. To reduce the encoding time while preserving the expected video quality has become a real challenge today for video transmission and streaming especially using low-powered devices. Motion estimation (ME) and motion compensation (MC) using variable-size blocks (i.e., intermodes) require 60-80% of total computational time. In this paper we propose a new efficient intermode selection technique based on phase correlation and incorporate into HEVC framework to predict ME and MC modes and perform faster intermode selection based on three dissimilar motion types in different videos. Instead of exploring all the modes exhaustively we select a subset of modes using motion type and the final mode is selected based on the Lagrangian cost function. The experimental results show that compared to HEVC the average computational time can be downscaled by 34% while providing the similar rate-distortion (RD) performance.

QMET: A new quality assessment metric for no-reference video coding by using human eye traversal

The subjective quality assessment (SQA) is an ever demanding approach due to its in-depth interactivity to the human cognition. The addition of no-reference based scheme could equip the SQA techniques to tackle further challenges. Existing widely used objective metrics-peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) or the subjective estimator-mean opinion score (MOS) requires original image for quality evaluation that limits their uses for the situation having no-reference. In this work, we present a no-reference based SQA technique that could be an impressive substitute to the reference-based approaches for quality evaluation. The High Efficiency Video Coding (HEVC) reference test model (HM15.0) is first exploited to generate five different qualities of the HEVC recommended eight class sequences. To assess different aspects of coded video quality, a group of ten participants are employed and their eye-tracker (ET) recorded data demonstrate closer correlation among gaze plots for relatively better quality video contents. Therefore, we innovatively calculate the amount of approximation of smooth eye traversal (ASET) by using distance, angle, and pupil-size feature from recorded gaze trajectory data and develop a new-quality metric based on eye traversal (QMET). Experimental results show that the quality evaluation carried out by QMET is highly correlated to the HM recommended coding quality. The performance of the QMET is also compared with the PSNR and SSIM metrics to justify the effectiveness of each other.

Fast Coding Strategy for HEVC by Motion Features and Saliency Applied on Difference Between Successive Image Blocks

Introducing a number of innovative and powerful coding tools, the High Efficiency Video Coding HEVC standard promises double compression efficiency, compared to its predecessor H.264, with similar perceptual quality. The increased computational time complexity is an important issue for the video coding research community as well. An attempt to reduce this complexity of HEVC is adopted in this paper, by efficient selection of appropriate block-partitioning modes based on motion features and the saliency applied to the difference between successive image blocks. As this difference gives us the explicit visible motion and salient information, we develop a cost function by combining the motion features and image difference salient feature. The combined features are then converted into area of interest AOI based binary pattern for the current block. This pattern is then compared with a previously defined codebook of binary pattern templates for a subset of mode selection. Motion estimation ME and motion compensation MC are performed only on the selected subset of modes, without exhaustive exploration of all modes available in HEVC. The experimental results reveal a reduction of 42i¾?% encoding time complexity of HEVC encoder with similar subjective and objective image quality.

A Novel No-reference Subjective Quality Metric for Free Viewpoint Video Using Human Eye Movement

The free viewpoint video (FVV) allows users to interactively control the viewpoint and generate new views of a dynamic scene from any 3D position for better 3D visual experience with depth perception. Multiview video coding exploits both texture and depth video information from various angles to encode a number of views to facilitate FVV. The usual practice for the single view or multiview quality assessment is characterized by evolving the objective quality assessment metrics due to their simplicity and real time applications such as the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM). However, the PSNR or SSIM requires reference image for quality evaluation and could not be successfully employed in FVV as the new view in FVV does not have any reference view to compare with. Conversely, the widely used subjective estimator- mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain knowledge, and many other factors that may actively influence on actual assessment. To address this limitation, in this work, we devise a no-reference subjective quality assessment metric by simply exploiting the pattern of human eye browsing on FVV. Over different quality contents of FVV, the participants eye-tracker recorded spatio-temporal gaze-data indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the proposed QMET performs better than the SSIM and MOS in terms of assessing different aspects of coded video quality for a wide range of FVV contents.

A novel depth edge prioritization based coding technique to boost-UP HEVC performance

In addition to the texture, multiview video employs the utilization of depth coding for the reconstruction of 3D video and Free viewpoint video. Standing on some texture-depth correlations, a number of methods in literature reuses texture motion vector for the corresponding depth coding to reduce encoding time by avoiding costly motion estimation process. However, texture similarity metric is not always equivalent to the corresponding depth similarity metric especially at edge levels. Since their approaches could not explicitly detect and encode acute edge motions of depth objects, eventually, could not reach the similar or improved rate-distortion (RD) performance against the High Efficiency Video Coding (HEVC) reference test model (HM). With a view to more accurate motion detection and modeling, the proposed technique exploits an extra Pattern Mode comprising a group of pattern templates (GPTs) with different rectangular and non-rectangular object shapes and edges compared to the existing HEVC block partitioning modes. Moreover, the proposed Pattern Mode only encodes the motion areas and skips the background areas. The experimental results show that the proposed technique could save 30% encoding time and improve average 0.1dB Bjontegard Delta peak signal-to-noise ratio (BD-PSNR) compared to the HM.

Foreground motion and spatial saliency-based efficient HEVC Video Coding

High Efficiency Video Coding (HEVC) could not provide real time facilities to the limited processing and battery powered electronic devices as its encoding time complexity increases multiple times compared to its predecessor. Numerous researchers contribute to address this limitation by reducing a number of motion estimation (ME) modes where they analyze homogeneity, residual and statistical correlation among different modes. Although their approaches save some encoding time, however, could not reach the similar rate-distortion (RD) performance with HEVC encoder as they merely depend on existing Lagrangian cost function (LCF) within HEVC framework. To overcome this limitation, in this paper, we capture visual attentive Foreground motion and salient region (FMSR) which are sensitive to human visual system for quality assessment. The FMSR features captured by visual attentive and dynamic background modeling are adaptively synthesized to determine a subset of candidate modes. This preprocessing phase is independent from LCF. Since the proposed technique can avoid exhaustive exploration of all modes with simple criteria, it can reduce 27% encoding time on average. With efficient selection of FMSR-based appropriate block partitioning modes, it can also improve up to 1.0dB peak signal-to-noise ratio (PSNR).