Objective Video quality assessment of Dirac and H.265

Transcription

1 Objective Video quality assessment of Dirac and H.265 A PROJECT UNDER THE GUIDANCE OF DR. K. R. RAO COURSE: EE MULTIMEDIA PROCESSING, SPRING 2016 SUBMITTED BY: SATYA SAI KRISHNA KUMAR AVASARALA Satyasai.avasarala@mavs.uta.edu DEPARTMENT OF ELECTRICAL ENGINEERING UNIVERSITY OF TEXAS AT ARLINGTON 1 P a g e

2 List of Acronyms and Abbreviations: AVC advanced video coding BBC British Broadcasting Corporation BD Bjontegaard Delta CBR constant bit rate DVD Digital Video Disc CODEC coder and decoder FR-Ext fidelity range extensions FSIM featured similarity index GM gradient magnitude HEVC high efficiency video coding HD High Definition HVS human visual system ISDN Integrated Services Digital Network IEC ISO IST ITU international electro-technical commission international organization for standardization integer sine transform international telecommunication union JPEG joint photographic experts group KTA Key Technical Area LAN Local Area Network LIVE laboratory for image and video engineering 2 P a g e

3 MICT media information and communication technology laboratory MPEG moving picture experts group MSE mean squared error PC phase congruency PSNR peak signal to noise ratio PWSSIM Perceptual Weighted Structural Similarity index PSTN Public Switched Telephone Network RGB SD red, green and blue Standard Definition SSIM structural similarity metric VBR variable bit rate VCEG video coding experts group ABSTRACT: The objective of this project is to study and implement the video coding standards those are HEVC [1] and DIRAC [3][4] and understand various techniques in video coding such as prediction, transform, quantization and coding. The main interest of this project would be objective quality assessment of the codecs. So, these video codecs will be tested based on various metrics such as computational time, PSNR, SSIM, PWSSIM, BD-Bit rate and BD- PSNR. The HM 16.3 [16], Schroedinger [20] from The WebM Project test models for HEVC and DIRAC respectively will be used for this purpose. Doubling coding efficiency corresponds to halving the bit rate necessary to represent video content with a given level of perceptual picture quality. The conventional metrics like PSNR and MSE are a measure of intensity and cannot measure the subjective fidelity [3]. In this project, PSNR and MSE will be of little interest. At the end of the project we will see to that HEVC has better bit-rate savings (50% reduced bit rate when compared with that of the previous version which is AVC) and also good throughput when compared with the other video coding standards. 3 P a g e

4 EVOLUTION OF VIDEO CODECS: Figure 1 : Evolution of video codecs [1] Figure 1 shows all the video coding standards developed till date. Major video coding standards have been developed by the International Standardization Organization / International Electro technical Commission (ISO/IEC) and the International Telecommunications Union Telecommunication Standardization Sector (ITU-T) [8]. Figure 1 shows a historical perspective for video coding standards development since the very first ITU-T H.120. The emergence of H.264/AVC doubled the coding efficiency from that of the MPEG-4 simple profile and has therefore gained wide industrial acceptance recently [8]. Further extensions of H.264/AVC include high profile, scalable video coding (SVC) extension and multiview video coding (MVC) extension [8]. Back in 2005, the ITU-T Video Coding Experts Group (VCEG) considered the future work beyond H.264/AVC [8]. Possible targets and scope of the standard were brainstormed and a software known as Key Technology Area (KTA) was developed and released in 2008 [8]. In 2009, the ISO/IEC Moving Picture Experts Group (MPEG) began a similar call for High- Performance Video Coding (HVC) [8]. KEY CONCEPTS OF VIDEO CODING: Color Spaces: The common color spaces for digital image and video representation are: 4 P a g e

5 RGB color space Each pixel is represented by three numbers indicating the relative proportions of red, green and blue colors Y Cr Cb color space Y is the luminance component, a monochrome version of color image. Y is a weighted average of R, G and B Where k is the weighting factors. The color information is represented as color differences or chrominance components, where each chrominance component is difference between R, G or B and the luminance Y. As the human visual system is less sensitive to color than the luminance component, Y Cr Cb has advantages over RGB space. The amount of data required to represent the chrominance component reduces without impairing the visual quality [10]. The popular pattern of sampling [10] is: 4:4:4 The three components Y: Cr: Cb has the same resolution, which is for every 4 luminance samples there are 4 Cr and 4 Cb samples. The popular patterns of sub-sampling [10] are: 4:2:2 For every 4 luminance samples in the horizontal direction, there are 2 Cr and 2 Cb samples. This representation is used for high quality video color reproduction. Figure 3 shows the sub-sampling pattern for 4:2:2. 4:2:0 The Cr and Cb each have half the horizontal and vertical resolution of Y. This is popularly used in applications such as video conferencing, digital television and DVD storage. Figure 2 shows the sub-sampling pattern for 4:2:0. Fig 2: 4:2:0 sub-sampling pattern [2]. 5 P a g e

6 Fig 3: 4:2:2 sub-sampling pattern and 4:4:4 sampling pattern [2]. H.265/HEVC: High Efficiency Video Coding (HEVC) [1] is an international standard for video compression developed by a working group of ISO/IEC MPEG (Moving Picture Experts Group) and ITU-T VCEG (Video Coding Experts Group). The main goal of HEVC standard is to significantly improve compression performance compared to existing standards such as H.264/Advanced Video Coding [3] in the range of 50% bit rate reduction at similar visual quality [6]. HEVC is designed to address existing applications of H.264/MPEG-4 AVC and to focus on two key issues: increased video resolution and increased use of parallel processing architectures [6]. It primarily targets consumer applications as pixel formats are limited to 4:2:0 8-bit and 4:2:0 10- bit. The new revised standard enables new use-cases with the support of additional pixel formats such as 4:2:2 and 4:4:4 and bit depth higher than 10-bit [2], embedded bit-stream scalability and 3D video [3]. 6 P a g e

7 HEVC ENCODER: ENCODER STEPS: Figure 4:HEVC encoder block diagram [5] The video encoder performs the following steps: Partitioning each picture into multiple units. Predicting each unit using inter or intra prediction, and subtracting the prediction from the unit. Transforming and quantizing the residual (the difference between the original picture unit and the prediction). Entropy encoding transform output, prediction information, mode information and headers. 7 P a g e

8 HEVC DECODER: DECODER STEPS: Figure 5: HEVC decoder block diagram [6] The video decoder performs the following steps: Entropy decoding and extracting the elements of the coded sequence Rescaling and inverting the transform stage. Predicting each unit and adding the prediction to the output of the inverse transform. Reconstructing a decoded video image. 8 P a g e

9 DIRAC Dirac video codec was initially developed by BBC Research [10]. It is an open source software project and is powerful and flexible despite using only small number of core tools [10]. The several features that Dirac offers are [8]: Multi-resolution transforms Inter and intra frame coding Frame and field coding Dual syntax CBR and VBR operations Variable bit depths. Multiple chroma sampling formats Lossless and lossy coding Choice of wavelet filters Simple stream navigation Dirac has three main strands [10]. First is a compression specification for the byte stream and the decoder [8]. Second is software for compression and decompression and third is the algorithms designed to support simple and efficient hardware implementations. Dirac despite being similar to many video coding systems had additionally adopted the combined effectiveness, efficiency and simplicity. The block diagram of Dirac encoder is shown in figure 8. Dirac is a hybrid motion-compensated state-of-the-art video codec that uses modern techniques such as wavelet transforms and arithmetic coding. It is an open technology designed to avoid patent infringement and can be used without the payment of license fees. It is well suited to the business model of public service broadcasters since it can be easily recreated for new platforms. Dirac is aimed at applications ranging from HDTV (high definition television) to web streaming. The Dirac decoder block diagram shown in figure 9. 9 P a g e

10 DIRAC ENCODER: Figure 8: DIRAC encoder block diagram [4] DIRAC DECODER: Figure 9: DIRAC decoder block diagram [4] 10 P a g e

11 PERFORMANCE METRICS: Video Quality Assessment using SSIM and FSIM: Digital images and videos are prone to different kinds of distortions during different phases like acquisition, processing, compression, storage, transmission, and reproduction [27]. This degradation results in poor visual quality. There are several metrics which are widely used to quantify the image quality like FSIM, SSIM, bitrates, PSNR and MSE [27, 28 and 29]. This project will primarily focus on metrics like SSIM, FSIM and bitrates. The other conventional metrics like PSNR and MSE will not be measured as they are directly dependent on the intensity of an image and do not correlate with the subjective fidelity ratings [3]. MSE cannot model the human visual system very accurately [4]. The measured parameters like FSIM and SSIM of Dirac and H.265 will be compared to study their comparative characteristics and make conclusions. SSIM is the quality assessment of an image based on the degradation of structural information [5]. The SSIM takes an approach that the human visual system is adapted to extract structural information from images [14]. Thus, it is important to retain the structural signal for image fidelity measurement. Figure 10 shows the difference between nonstructural and structural distortions. The nonstructural distortions are changes in parameter like luminance, contrast, gamma distortion, and spatial shift and are usually caused by environmental and instrumental conditions occurred during image acquisition and display [27]. On the other hand, the structural distortions embrace additive noise, blur and lossy compression [14]. The structural distortions change the structure of an image [14]. Figure 11 explains the measurement system used in the calculation of SSIM. Figure 10.Difference between nonstructural and structural distortions [14] 11 P a g e

12 Figure 11.Block diagram of SSIM measurement system [28] SSIM is based on the evaluation of three different metrics like luminance, contrast, and structure which are described mathematically by equations (1), (2), and (3) respectively [27] (1) (2) Here, (3) µx and µy= local sample means of x and y respectively σx and σy= local sample standard deviations of x and y respectively 12 P a g e

13 σxy = local sample correlation coefficient between x and y C1, C2, and C3 = constants that stabilize the computations when denominators become small. General form of SSIM index can be obtained by combining equations (1), (2) and (3) [27] (4) Here, α, β, and γ are parameters that mediate the relative importance of those three components. Using α = β = γ = 1. We get [7], (5) FSIM : FSIM incorporates the chrominance information. FSIM can be mathematically modeled as shown in equation 6 [28] (6) Here, SL(x) = overall similarity between reference image and distorted image FSIM can be mathematically modeled as shown in equation (7) Here, λ> 0 is the parameter used to adjust the importance of the chrominance components. 13 P a g e

14 PSNR : PSNR PSNR YUV is mostly used to evaluate the video quality for 4:2:0 format only [21] (8) while the individual values for PSNR Y, PSNR U, PSNR V are calculated as follows [17]. Where B = number of bits per sample MSE= Mean squared error BD-PSNR and BD-Bitrate: BD-PSNR This computes the average PSNR differences in db for the same bit rate [17]. The average PSNR difference between two R-D curves is approximated by the difference between the integrals of the fitted R-D curves divided by the integration interval (delta D). BD Bit rate This computes the bit difference between the two R-D curves for a given bit rate [17]. PWSSIM: PWSSIM - It uses perceptual spatial information as a way of weighting the most important visual regions [17]. Spatial Information is calculated as follows. 14 P a g e

15 (10) PWSSIM is given by: (11) Where, µs = mean of the gradient magnitude of a block N = Number of pixels in the block. f= Frame h=height of the frame D= Number of frames Factors that affect the video quality: Compression Transmission errors Display Reproduction systems Pre/post processing And many more Why go for objective video quality assessment: Subjective video quality assessment methods are undoubtedly reliable methods than the objective methods [28]. Complexity is high in subjective methods. 15 P a g e

16 Need to follow strict evaluation conditions. Unable to provide instantaneous results. Due to all the above factors objective quality assessment algorithms have been developed [27]. Objective quality methods: Media layer model: The Media layer models use the speech or video signal to compute the Quality of Experience (QoE). These models do not require any information about the system under testing, hence can be best applied to scenarios such as codec comparison and codec optimization. Parametric packet-layer model: Unlike the media layer models, the parametric packet-layer models predict the QoE only from the packet-header information and do not have access to media signals. But this forms a lightweight solution for predicting QoE as it does not have to process the media signals. Parametric planning model: These models make use of quality planning parameters for networks and terminals to predict the QoE. As a result they require a priori knowledge about the system that is being tested. Bitstream layer model: These models use encoded bitstream information and packet-layer information that is used in parametric packet-layer models for measuring QoE. Hybrid model: These models mainly combine two or more of the preceding models. The block diagram of objective quality methods is shown in figure 12. Objective quality methods: 16 P a g e

17 Figure 12: Block diagram of objective quality assessment methods [27] Media layer models: Figure 13: Block diagram of media layer models [28] 17 P a g e

18 (a) Full reference model: In this model we have both the reference video as well as the distorted video for video quality assessment. (b) Partial reference model: In this model we have partial characteristics that are related to the reference video and the distorted video to measure the video quality. (c) No reference model: In this model, we have no reference video. We only have the distorted video to evaluate the quality of the video. For instance, during the development and prototyping process of video transport systems, the original video can be delivered offline for full reference quality assessment at the receiver, or the received distorted video data can be reliably (without any further bit loss or modifications) delivered back to the sender. In contrast, for real-time quality assessments at the receiver without availability of the original video data, low-complexity reduced reference or no-reference methods are needed. The objective methods can also be classified in terms of their usability in the context of adaptive streaming solutions [29], [28] as out-of-service methods and in-service methods. In the case of out-of-service methods, no time constraints are imposed and the original sequence can be available. Full-reference visual quality assessment metrics and high-complexity non real-time RR and NR metrics fall within this class. On the other hand, the in-service methods place strict time constraints on the quality assessment and are performed during streaming applications. Video quality databases: VQEG FR-TV Phase I Database: It is the oldest public database on video quality applied to MPEG-2 and H.263 video with two formats: 525@60Hz and 625@50Hz in this database. The resolution for video sequence 525@60Hz is pixels and pixels for 625@50Hz. The video format is 4:2:2. The subjective quality scores provided are DMOS, ranging from 0 to 100. IRCCyN/IVC 1080i Database contains 24 contents. For each content, there is one reference and seven different compression rates on H.264 video. The resolution is pixels, the display mode is interleaving and the field display frequency is 50Hz. The provided subjective quality scores are MOS, ranging from 1 to 5. IRCCyN/IVC SD RoI Database contains six reference videos and 14 HRCs (i.e., 84 videos in total). The HRCs are H.264 coding with or without error transmission simulations. The contents of this database are SD videos. The resolution is pixels, the display mode is interleaving, and the field display frequency is 50Hz with MOS from 1 to 5. EPFL-PoliMI Video Quality Assessment Database contains 12 reference videos (6 in CIF, and 6 in 4CIF), and 144 distorted videos, which are encoded with H.264/AVC and corrupted by simulating the packet loss due to transmission over an error-prone network. For 18 P a g e

19 CIF, the resolution is pixels, and frame rate is 30 fps. For 4CIF, the resolution is pixels, and frame rates are 30 fps and 25 fps. For each of the 12 original H.264/AVC videos, they have generated a number of corrupted ones by dropping packets according to a given error pattern. To simulate burst errors, patterns have been generated at six different packet-loss rates (PLR) and two channel realizations have been selected for each PLR. LIVE Video Quality Database [28] includes 10 reference videos. All videos are 10s long, except for Blue Sky. The Blue Sky sequence is 8.68s long. The first seven sequences have a frame rate of 25 fps, while the remaining three (Mobile & Calendar, Park Run, and Shields) have a frame rate of 50 fps. There are 15 test sequences from each of the reference sequences using four different distortion processes simulated transmission of H.264 compressed videos through error-prone wireless networks and through error-prone IP networks, H.264 compression, and MPEG-2 compression. All video files have planar YUV 4:2:0 formats and do not contain any headers. The spatial resolution of all videos is pixels. LIVE Wireless Video Quality Assessment Database has 10 reference videos, and 160 distorted videos, which focus on H.264/AVC compressed video transmission over wireless networks. The video is YUV 4:2:0 formats with a resolution of and a frame rate of 30 fps. Four bitrates and four packet-loss rates are performed. However, this database has been taken offline temporarily because it has limited video level contents and a tendency to cluster at correlation for most objective metrics. MMSP 3D Video Quality Assessment Database contains stereoscopic videos with a resolution of pixels and a frame rate of 25 fps. Various indoor and outdoor scenes with a large variety of color, texture, motion, and depth structure have been captured. The database contains 6 scenes, and 20 subjects participated in the test. For each of the scenes, 5 different stimuli have been considered corresponding to different camera distances (10, 20, 30, 40 and 50 cm). MMSP Scalable Video Database is related to two scalable video codecs (SVC and wavelet-based codec), three HD contents, and bit rates ranging between 300 kbps and 4Mbps. There are three spatial resolutions ( , and ), and four temporal resolutions (6.25 fps, 12.5 fps, 25 fps, and 50 fps). In total, 28 and 44 video sequences were considered for each codec, respectively. The video data are in the YUV 4:2:0 formats. VQEG HDTV Database has four different video formats 1080p at 25 and fps, 1080i at 50 and fps. The impairments are restricted to MPEG-2 and H.264, with both coding-only error and coding-plus-transmission error. 19 P a g e

20 VQA Metrics MOVIE: The MOVIE uses optical flow estimation to adaptively guide spatial temporal filtering using three-dimensional (3D) Gabor filter banks. The key differentiation of this method is that a subset of filters is selected adaptively at each location based on the direction and speed of motion, such that the major axis of the filter set is oriented along the direction of motion in the frequency domain. The video quality evaluation process is carried out with coefficients computed from these selected filters only. One component of the MOVIE framework, known as the Spatial MOVIE index, uses the output of the multi-scale decomposition of reference and test videos to measure spatial distortions in the video. The second component of the MOVIE index, known as the Temporal MOVIE index, captures temporal degradations in the video. The Temporal MOVIE index computes and uses motion information from the reference video, and evaluates the quality of the test video along the motion trajectories of the reference video. Finally, the Spatial MOVIE index and the Temporal MOVIE index are combined to obtain a single measure of video quality known as the MOVIE index. DVQ: The DVQ accepts a pair of video sequences and computes a measure of the magnitude of the visible difference between them. The first step consists of various sampling, cropping, and color transformations that serve to restrict processing to a region of interest (ROI) and to express the sequence in a perceptual color space. This stage also deals with de-interlacing and degamma-correcting the input video. The sequence is then subjected to a blocking and a discrete cosine transform (DCT), and the results are transformed to local contrast. Then, the next steps are temporal, spatial filtering, and a contrast masking operation. Finally, the masked differences are pooled over spatial, temporal and chromatic dimensions to compute a quality measure. V-Factor: V-Factor (NR, packet-analysis-based metric) [27] is a real-time, packet-based VQM, which works without the need of references. In this metric is primarily used in MPEG-2 and H.264 video streaming over IP networks. First, it inspects several parts of the video stream, including the transport stream (TS) headers, the packetized elementary stream (PES) headers, the video coding layer (VCL) and the decoded video signal. Then, it analyzes the bitstream to obtain static parameters, such as the frame rate and the image size. The dynamic parameters (e.g., variation of quantization steps) are also obtained along with the analysis. The final video quality is estimated based upon the content characteristics, compression methods, bandwidth constraints, delays, jitters and packet loss. Among these six factors, the first three are affected by video impairments and the last three are caused by network impairments. In addition, this metric also analyzes real-time network impairments to calculate the packet loss probability ratio by using hidden Markov models. ST-MAD: As the name suggests, it includes both spatial and temporal parts. In the first step, they used a temporal approach to find the matching regions in adjacent frames. One important change from existing motion estimation methods during this step is to use CW-SSIM instead of 20 P a g e

21 the mean absolute difference to compute the motion vectors. This will increase the precision of finding the matching regions. In the second step, a spatial method is used to compute the quality of the matching regions extracted via the temporal approach. The visual attention map (VAM) is used to weight each sub-block in the luminance channel based on the importance. In the final step, the video quality is estimated according to the values obtained from both the spatial and temporal domains, and quality of experience (QoE) is introduced as a function related to the motion activity density group of the video to control the pooling function. STAQ: First, a spatiotemporal slice (STS) image is constructed from the time-based slices of the reference and distorted videos. The detailed procedure is as follows: A single column or row of the frame is extracted for each video frame, and these columns (or rows) are stacked from left to right (or top to bottom) to become a STS image. Then ST-MAD estimates motion-based distortions by using MAD s appearance base model to STS images. Next, it gives larger weights to the fast-moving regions by applying optical-flow algorithm. Finally, it employs a combination rule to add spatial and temporal distortions together. Experimental results show that ST-MAD performs better than other state-of-the-art quality metrics in LIVE Video Quality Database, especially on H.264 and MPEG-2 distorted videos. However, MOVIE only outperforms ST-MAD for wireless distorted videos. Results: Test sequence 1: Bus_176x144_25.yuv 21 P a g e

22 QP=32 QP=32 but with random access _main10 22 P a g e

23 QP=10 Table 1:Bus.yuv metrics summary 23 P a g e

24 Test sequence 2: Foreman_352x288_30.yuv QP=32 24 P a g e

25 QP=10 Table 2: Foreman.yuv metrics summary 25 P a g e

26 Test platform Processor 2.4Ghz RAM-4GB 64-bit operating system OS-windows 10 Home N PROFILES USED FOR ASSESSMENT: The HM 16.3 [16] and Schroedinger [20] are the softwares that I have used for HEVC and Dirac respectively in this project. Various test sequences have been encoded in this project with the necessary profile settings to get the results. 26 P a g e

27 Test sequences that will be used: 1. Basketballdrive [18] Figure 12: BasketballDrill_832x480_50.yuv 2. Cactus [20] Figure 14: Cactus_1920x1080_30.yuv 27 P a g e

28 3.Racehorses [19] Figure 15: RaceHorses_416x240_30.yuv Conclusions For a given objective quality assessment metric [28] to be reliable, its score should be equivalent to that of the subjective quality metric and also outlier ratio [27] should be low. 28 P a g e

29 REFERENCES: [1] K. R. Rao et al, Video Coding Standards: AVS China, H.264/MPEG-4Part10, HEVC, VP6, DIRAC and VC-1, Springer, [2] J-R Ohm et al, "Comparison of the Coding Efficiency of Video Coding Standards - including High Efficiency Video Coding (HEVC) ", IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, Issue: 12, pp , Dec.2012 [3] Website on HEVC [4] I.E.G Richardson, The H.264 advanced video compression standard. Chichester, West Sussex: Wiley, [5] K.R. Rao and J.J.Hwang, Techniques and standards for Image Video and Audio Coding, Prentice Hall, [6] F. Bossen et al, "HEVC Complexity and Implementation Analysis", IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 12, pp , Dec [7] V. Sze, M. Budagavi and G. J.Sullivan "High Efficiency Video Coding (HEVC): Algorithms and Architectures", Springer, [8] -Website on Dirac [9] J. Choi and Y. Ho, "Efficient residual data coding in CABAC for HEVC lossless video compression", Signal, Image and Video Processing, vol. 9, no. 5, pp , Dec [10] A. Ravi and K.R Rao, "Performance Analysis and Comparison of the Dirac Video Codec with H.264/MPEG-4 Part 10 AVC", Int. J. Wavelets Multi resolution Inf. Process., vol. 09, no. 04, pp , July.2011 [11] G.J. Sullivan et al, Standardized Extensions of High Efficiency Video Coding (HEVC), IEEE Journal of selected topics in Signal Processing, Vol. 7, No. 6, pp , Dec [12] Access to JM 19.0 Reference Software: [13] T. Wiegand, et al, Overview of the H.264/AVC Video Coding Standard, IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, pp , July [14] Visual studio download for students for free- [15] Z. Wang et al, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, Vol. 13, No. 4, pp , Apr [16] Access to HM 16.3 Reference Software: 29 P a g e

30 [17] P. Hanhart and T. Ebrahimi, "Calculation of average coding efficiency based on subjective quality scores", Journal of Visual Communication and Image Representation, vol. 25, no. 3, pp , Apr [18] - HEVC test sequences. [19] - test sequences [20] - Access to DIRAC reference software. [21] G. Bjøntegaard, Calculation of Average PSNR Differences Between RD Curves, document VCEG-M33, ITU-T SG 16/Q 6, Austin, TX, Apr [22] B. Li, G. J. Sullivan, and J. Xu, Compression performance of high efficiency video coding (HEVC) working draft 4, in Proc. IEEE Int. Conf. Circuits Syst., pp , May [23] K. Ramchandran and M.Vetterli, Rate-distortion optimal fast thresholding with complete JPEG/MPEG decoder compatibility, IEEE Trans. Image Process., vol. 3, no. 5, pp , Sep [24] Tortoise SVN download- [25] Tut6. D. Grois, et al, HEVC/H.265 Video Coding Standard including the Range Extensions, Scalable Extensions, and Multiview Extensions, (Tutorial), IEEE ICCE, Berlin, Germany, 6 9 Sept [26] Tut7. D. Grois, et al, HEVC/H.265 Video Coding Standard (Version 2) including the Range Extensions, Scalable Extensions, and Multiview Extensions, (Tutorial) Sunday 27 Sept 2015, 9:00 am to 12:30 pm), IEEE ICIP, Quebec City, Canada, Sept The tutorial below is for personal use only. Password: a2fazmgnk 9c1a17964bf881 [27] R. Dosselmann and X. Yang, "A comprehensive assessment of the structural similarity index", Signal, Image and Video Processing, vol. 5, no. 1, pp , Sept [28] L. Zhang et al, FSIM: A feature similarity index for image quality assessment, IEEE Transactions on Image Processing, vol.20, no.8, pp , Aug P a g e

31 [29] Z. Wang et al, Multiscale structural similarity for image quality assessment, Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2003, vol.2, pp , 9-12 Nov [30] X. Ran and N. Farvardin, A perceptually-motivated three-component image model - part I: description of the model, IEEE Transactions on Image Processing, vol.4, no.4, pp , Apr [31] C. Chukka, A universal image quality index and SSIM comparison [Online]. Available: 31 P a g e