ACRONYMS - The University of Texas at Arlington – UT ... · Web viewDiscrete Cosine Transform Interpolation Filters DDCT Directional Discrete Cosine Transform DSP Digital Signal

Topics in Signal ProcessingCourse ID: EE5359

Interim Report: HEVC Lossless Coding and

Improvements

SUBMITTED BY: SUJATHA GOPALAKRISHNAN STUDENT ID: 1001024145

Table of ContentsACRONYMS...................................................................................................................................3HEVC..............................................................................................................................................5Block Diagram HEVC.....................................................................................................................5HEVC Lossless Coding...................................................................................................................6Block Diagram HEVC Lossless Coding..........................................................................................7Basic Definitions.............................................................................................................................7

H.264 [1] [2]................................................................................................................................7Inter Frame...................................................................................................................................7Intra Frame...................................................................................................................................8Loop Filters..................................................................................................................................8

De-blocking Filter....................................................................................................................8Sample Adaptive Offset...........................................................................................................8

Block-Based Angular Intra Prediction.........................................................................................8Sample-Based Angular Intra Prediction......................................................................................9Coding Tools...............................................................................................................................9

LCU/CTU................................................................................................................................9Parallel Processing...................................................................................................................9Entropy Coding........................................................................................................................9Motion Estimation.................................................................................................................10Motion Compensation...........................................................................................................10

DCT for HEVC lossless compression...........................................................................................11Improved HEVC lossless compression using Two-Stage coding.................................................11Algorithm of Sample Based Angular Intra Prediction..................................................................12Pixel-based averaging predictor....................................................................................................13NLM Algorithm.............................................................................................................................14Low-Complexity Pixel wise Predictor Implementation................................................................16Test Sequences...............................................................................................................................17

Test Sequence 1.........................................................................................................................17Test Sequence 2.........................................................................................................................18

Project Results...............................................................................................................................19Future Work...................................................................................................................................21References......................................................................................................................................21

ACRONYMS2D Two dimension3D Three dimensionACM MoVid

Association for Computer Machinery Mobile Video

AHG Ad Hoc GroupsAIF Adaptive Interpolation FilterALF Adaptive Loop FilterAMVP Advanced Motion Vector PredictionAPIF Adaptive Pre-Interpolation FilterASIC Application-Specific Integrated CircuitAVC Advanced Video CodingAVS Audio Video StandardBBC British Broadcasting CorporationBD Bjontegaard DistortionBL Base Layerbpp Bits per pixelBS Boundary StrengthCU Coding UnitCI Confidence IntervalCABAC Context Adaptive Binary Arithmetic CodingCPU Central Processing UnitCRA Clean Random AccessCSVT Circuits and Systems for Video TechnologyCU Coding UnitDCT Discrete Cosine TransformDCTIF Discrete Cosine Transform Interpolation FiltersDDCT Directional Discrete Cosine TransformDSP Digital Signal ProcessingDST Digital Sine TransformEC Error ConcealmentFIR Finite Impulse ResponseFPGA Field Programmable Gate Arrayfps Frames per secondGPU Graphics Processing UnitHDR High Definition RangeHEVC High efficiency video codingHEVStream High Efficiency Video StreamHTTP Hyper Text Transfer ProtocolICIEA IEEE Conference on Industrial Electronics and ApplicationsIEEE Institute of Electrical and Electronics EngineersINTDCT Integer Discrete Cosine Transformintra HE Intra high efficiency

IPTV Internet Protocol TelevisionIS & T Information Systems and TechnologyISO International Organization for StandardizationITU-T Telecommunication Standardization Sector of the International

Telecommunications UnionIVMSP Image, Video, and Multidimensional Signal ProcessingJCTVC Joint Collaborative Team on Video CodingJM Joint ModelJPEG Joint Photographic Experts GroupJPEG-XR JPEG extended rangeJSVM Joint Scalable Video ModelJTC Joint Technical CommitteeLR Low ResolutionMbit/s Megabit per secondMC Motion CompensationMDDCT Modified Directional Discrete Cosine TransformMDDT Mode-Dependent Directional TransformME Motion EstimationMJPEG Motion JPEGMMSP Multimedia Signal ProcessingMPEG Moving Picture Experts GroupMpixel MegapixelMpm Most Probable ModesMV Motion VectorNAB National Association of BroadcastersNALNLM

Network Abstraction LayerNon-Local Means

PCM Pulse Code ModulationPSNR Peak-to-peak signal to noise ratioPU Prediction UnitQP Quantizer parameterRD Rate DistortionRDOQ Rate-distortion optimized quantizationRDPCM Residual Differential Pulse Code ModulationROT Rotational TransformRTP Real-time Transport ProtocolSAOSAP

Sample adaptive offsetSample based Angular Intra-Prediction

SHVC Scalable High Efficiency Video CodingSVC Scalable Video CodingSELC Sample based weighted prediction for Enhancement Layer CodingSIP Signal and Image ProcessingSSVC Spatially Scalable Video CodingTB Transform BlockTU Transform Unit

HEVC High Efficiency Video Coding (HEVC) [1] [2] is a video compression standard, a

successor to H.264/MPEG-4 AVC [22]. HEVC is said to double the data compression ratio compared to H.264/MPEG-4 AVC [1] at the same level of video quality [2].

The design of most video coding standards is primarily aimed at having the highest coding efficiency

HEVC benefits from the use of larger Coding Tree Unit (CTU) sizes. The HEVC video coding layer uses the same "hybrid" approach used in all modern video

standards, starting from H.261 [1], in that it uses inter-/intra-picture prediction and 2D transform coding.

The main goal of the HEVC standardization effort is to enable significantly improved compression performance relative to existing standards, in the range of 50% bit rate reduction for equal perceptual video quality [10] [11].

Block Diagram HEVC

Figure 1: HEVC Encoder [2]

Figure 2: HEVC Decoder Block Diagram [3]

Some differences in HEVC [1][2] are coding tree units instead of macro blocks, single entropy coding methods-Context Adaptive Binary Arithmetic Coding (CABAC) [15] method and features like tiles , wave front parallel processing and dependent slices to enhance parallel processing.

HEVC Lossless Coding The lossless coding mode of HEVC main profile bypasses transform quantization and in-

loop filters as shown in the fig.2 [4][19]. Comparing it with non-lossless coding mode, it has smallest quantization parameter

value. Lossless coding mode provides perfect fidelity and average bit rate reduction. Outperforms the existing lossless compression solution such as JPEG-2000 [22] and

JPEG-LS [22]. It can prevent accumulation of quantization errors in repeated encoding and decoding

operations of video editing In this method it is essential to preserve numerical video data with fewer bits. DCT coefficients i.e., float-point numbers have to be quantized instead of DCT. Lossless video coding is used when perfect preservation of video data is required [29]. It employs Sample Angular-based Intra-Prediction (SAP) [4].

Same prediction mode signaling method. Same interpolation method of HEVC. Uses adjacent neighbors as reference shown in fig.8.

Prediction residuals are coded with the entropy coder in the spatial domain [29].

Block Diagram HEVC Lossless Coding

Figure 3: HEVC lossless Algorithm Block Diagram [4]

The blocks that are marked bypass are not being used when implementing a HEVC [1] [2] lossless algorithm, thereby providing average bit rate reduction.

Basic DefinitionsH.264 [1] [2]H.264/MPEG-4 AVC [1] [2] [22] is a block-oriented, motion-compensation based video compression standard.

Inter FrameAn inter frame is a frame in a video compression stream which is expressed in terms of one or more neighboring frames. The "inter" part of the term refers to the use of Inter frame prediction.

Intra FrameThe term intra-frame refers to the various lossless and lossy compression techniques that happens relative to information which is contained only within the current frame and not relative to any other frame in the video sequence.

Loop FiltersHEVC [1] specifies two loop filters that are applied sequentially; the de-blocking filter (DBF) [4] applied first and the sample adaptive offset (SAO) filter applied afterwards. Both loop filters are applied in the inter-picture prediction loop, i.e. the filtered image is stored in the decoded picture buffer (DPB) as a reference for inter-picture prediction.

De-blocking Filter The DBF is similar to the one used by H.264/MPEG-4 AVC [1] [2], but with a simpler

design and better support for parallel processing. DBF first apply horizontal filtering for vertical edges to the picture and only after that

does, it apply vertical filtering for horizontal edges to the picture. This allows for multiple parallel threads to be used for the DBF [1].

Sample Adaptive OffsetThe SAO filter is applied after the DBF and is designed to allow for better reconstruction of the original signal amplitudes by applying offsets stored in a lookup table in the bit stream.

Block-Based Angular Intra PredictionIt is a method of computing predicted samples produced by PU when lossless coding is not enabled. It is defined to exploit spatial sample redundancy in intra coded CUs. As shown in the fig.4, a total of 33 angles are defined for the angular prediction, which can be categorized into two classes: vertical and horizontal angular predictions as illustrated [14].

Figure 4: Block Based Angular Intra Prediction in HEVC [4]

Sample-Based Angular Intra PredictionIt is a method of computing predicted samples produced by PU when lossless coding is enabled. It is explained detail in the following.

Coding ToolsCoding efficiency is the ability to encode video at the lowest possible bit rate while maintaining a certain level of video quality. This could be achieved with the following coding tools.

LCU/CTUCoding tree unit (CTU) is the basic processing unit of the HEVC video standard and conceptually corresponds in structure to macroblock units, which were used in several previous video standards. CTU is also referred to as largest coding unit (LCU) [1] [2]. In the HEVC, one frame is divided into a series of non-overlapped Coding Tree Unit (CTU) [9] [12].

Figure 5: Division of a CTB into CBs and transform blocks TB [2]

Parallel ProcessingA picture is divided into tiles. Main purpose of these tiles is that, they can be decoded /encoded individually in a simultaneous way called parallel processing. Parallel computing is basically a technique in which multiple computation tasks are assigned to multiple processes and process the job simultaneously. The basic approach for parallel processing is to break the task into multiple smaller tasks and further assign each task to each of the thread which performs required operations in parallel. Parallelization can sometimes get complicated due to race conditions, data dependency, synchronization and communication among different threads [13].

Entropy CodingHEVC uses a context-adaptive binary arithmetic coding (CABAC) algorithm that is fundamentally similar to CABAC in H.264/MPEG-4 AVC. CABAC is the only entropy encoder method that is allowed in HEVC while there are two entropy encoder methods allowed by H.264/MPEG-4 AVC. CABAC and the entropy coding of transform coefficients in HEVC are designed for a higher throughput than H.264 while maintaining higher compression efficiency for larger transform block sizes relative to simple extensions [15][16]. These techniques include reducing context coded bins, grouping bypass bins, grouping bins with the same context, reducing context selection dependencies, reducing total bins, and reducing parsing dependencies.

It also describes reductions to memory requirements that benefit both throughput and implementation costs.

Motion EstimationMotion estimation [5] is an essential process in many video coding standards like MPEG-2, H.264/AVC and HEVC [1] [2]. Motion estimation has been used at the encoder. Motion Estimation itself consumes more than 50% coding complexity or time to encode. To reduce the computation time, many fast motion estimation Algorithms were proposed and implemented [5]. HEVC allows for two MV modes which are Advanced Motion Vector Prediction (AMVP) and merge mode. AMVP uses data from the reference picture and can also use data from adjacent prediction blocks. The merge mode allows for the MVs to be inherited from neighboring prediction blocks. Merge mode in HEVC is similar to "skipped" and "direct" motion inference modes in H.264.

Motion estimation process in HEVC consumes more than 50% coding complexity or time to encode with equal perceptual quality [6] [7]. Many block based motion estimation algorithms [8] [9] are proposed and also implemented to reduce the computation time.

Figure 6: Illustration of Motion Estimation process [5]

Motion CompensationThe interpolation of fractional luma sample positions HEVC uses separable application of one-dimensional half-sample interpolation, with an 8-tap filter or quarter-sample interpolation with a 7-tap filter [5]. While H.264/MPEG-4 AVC[1][2] uses a two-stage process that first derives values at half-sample positions, using separable one-dimensional 6-tap interpolation followed by integer rounding; then applies linear interpolation between values at nearby half-sample positions to generate values at quarter-sample positions. HEVC has improved precision due to the longer interpolation filter and the elimination of the intermediate rounding error. As in H.264/MPEG-4

AVC [1] [2], a scaling and offset operation may be applied to the prediction signal(s) in a manner Known as weighted prediction [1].

DCT for HEVC lossless compression DCT is applied to prediction residuals. DCT coefficients are quantized. Quantized DCT coefficients and quantization error are coded. Coding of each unit is performed by Adaptive Quantization parameters.

Improved HEVC lossless compression using Two-Stage coding Block of residual signal is separated into two parts:

Part 1: Quantized DCT coefficients. Part 2: Quantization error.

Quantized coefficients are used to reconstruct a lossy decoded block which is subtracted from the residual block.

Quantization error is encoded as the spatial block.

Figure 7 : Two stage lossless coding [30]

Table 1: Coded block flag for two-stage coding [30].

Table 2: Performance of two-stage coding [30].

Algorithm of Sample Based Angular Intra PredictionThe SAP [4] is designed to better exploit the spatial redundancy in the lossless coding mode by generating intra prediction samples from adjacent neighbors. The design principle here is very similar to the sample-based DPCM in [21] [4] H.264/MPEG-4 AVC [20] [4] lossless coding, but SAP [4] is fully harmonized with the HEVC block-based angular intra prediction, and can be applied to all the angular intra prediction modes specified in HEVC [4].

As shown in the fig.8 SAP is performed sample by sample. The adjacent neighboring samples,   of the current sample   in the current PU are used for prediction. That is, the reference samples used for prediction are not limited to those boundary reference samples from the left and upper neighboring PUs. The SAP has to be processed in a predefined order to ensure the availability of these adjacent neighbors for prediction.

Figure 8: Algorithm of SAP [4]

Pixel-based averaging predictor Lossless video compression of noisy video content can be improved if the noise within the video is considering the compression [32]. In HEVC lossless coding block-wise processing is not needed, pixel-wise prediction could be performed for better spatial correlation within the image or a video signal [31].

De-noised intra prediction scheme is used, where de-noising is performed by the predictor instead of removing the noise. Non-Local Means (NLM) algorithm is used for de-noising [33].

Figure 9: NLM algorithm for image de-noising [32].

Pixel-wise prediction is the combination of linear predictors with exponentially decaying weights from NLM algorithm.

Developed predictor results in a weighted average of surrounding pixels. Other non-local predictors e.g., forward adaptive scheme where intra-frame motion

compensation is performed [34] [32] or a backward adaptive scheme where template matching is performed for prediction [35] [36] [32], are designed for block-wise lossy prediction in H.264/AVC and thus are not efficient for lossless compression.

NLM AlgorithmThe Non-Local Means (NLM) [31] algorithm has been introduced in [32] for image de-noising. In NLM de-noising, the estimate for a de-noised version of a noisy pixel is established by averaging all the pixels in the local as well as non-local neighborhood. The process of NLM [31] de-noising is illustrated in Fig 9. In the illustrated ex-ample, the pixel g[i] should be de-noised, where i=(x,y) is the two-dimensional coordinate. Therefore all pixels in the support area S are averaged depending on their similarity to g[i].

The similarity between the pixels is measured by a certain mean distance of the pixels in the surrounding area, which is illustrated with the square around the pixels g[j1], g[j2] and g[j3]. For example the pixel g[j3] will get a higher weight than the pixels g[j1] and g[j2] because the pixels around g[j3] are more similar to the surrounding pixels of g[i]. The formal description of the originally proposed NLM algorithm is given in the following. The averaging process is described by

ΡNLM[i] = ∑j∊s w[I,j], g[j] [31]

In order to adapt the NLM algorithm for prediction purposes, some modifications have to be made which follow the causal relations in video encoding. We construct a weighted average of the causal pixels (i.e., S contains only the coordinates of causally available pixels) for prediction of pixel X. 

To measure the similarity of the candidate pixels to the pixel which is to be predicted, we perform a distance calculation. However, only the causal pixels around X without X itself can be used for the patch describing the neighborhood of X for the similarity measure i.e., No contains only the shifts to coordinates of causally available pixels.

For example different sized patches as illustrated in Fig 10. could be used. The same patches have to be used for the possible candidates in the neighborhood for distance calculation. For possible neighborhoods from where the prediction is performed, we could use the same shapes as are used for patches. For example Neighbor-hood 6 would mean that we use the same shape as is illustrated for Patch6 in Fig 10.

An example of a possible constellation for the NLM predictor is given in Fig 11. In this example, Patch2 and Neighborhood6 are used in the prediction process. It means that the pixels a … z are used for weighted average prediction of the pixel X.

Figure 10: Casual patches for NLM predictor [31]

Figure 11: Patch 2 and neighborhood 6 [31]

Low-Complexity Pixel wise Predictor Implementation Run time for prediction is proportional to the neighborhood size or the patch size. If the patch becomes larger, structural complexity of the patch becomes higher, so it

becomes harder to find similar patches. Hence patch and neighborhood sizes are reduced in NLM predictor. Results of the investigated parameter constellations a, b, h, dSSE and dSAD are shown in

Table 3 [32].

Table 3: Compression results of the proposed pixel-wise prediction [32].

Test SequencesSequences are obtained from [29] and experimented to obtain the performance based on various parameters described as follows.

Test Sequence 1

Figure 12: Race horse sequence [29]

Test Sequence Resolution Frame rate (fps)RaceHorses_416x240_30.yuv 416 x 240 30

Test sequence

Intra Profile

Random Access Profile

BD- % Bit rate

reduction

BD- PSNR

Race horse sequence

PSNR (dB) 34.2442 33.7342

-22.4269 1.483Bitrate(kbps) 1842.6421 371.49

Encoding time(sec)

24.332 119.764

Decoding time(sec)

0.840 4.134

Test Sequence 2

Figure 13: Basketball drill sequence [29]

Test Sequence Resolution Frame rate (fps)

BasketballDrill_832x480_50.yuv 832 x 480 50

Test sequence Intra Profile

Random Access Profile

BD- % Bit rate reduction

BD- PSNR

Basketball drill

PSNR (dB) 37.2947 35.7193

-32.8763 1.956Bitrate(kbps) 5941.7732 817.87

Encoding time(sec)

97.539 347.891

Decoding time(sec)

1.2 4.28

Project Results

BD-PSNR (dB) for the test sequences

Race horse (1.483) Basket ball drill (1.956)0

0.5

1

1.5

2

2.5

BD- PSNR (dB) HEVC Lossless Coding

BD- PSNR (dB)

Test Sequences

BD- P

SNR(

dB)

BD-% Bit rate reduction for test sequences

Race horse (-22.4269) Basket ball drill (-32.8763)

-35

-30

-25

-20

-15

-10

-5

0

BD- Bitrate % for HEVC Lossless coding

%BD- Bitrate

Test Sequenes

%BD

- Bitr

ate

Test sequence 1 [29]: Encoding and Decoding time (secs) for Intra and Random access profiles

Racehorse_Intra Racehorse_Random Access0

20

40

60

80

100

120

140

Encoding Time vs Decoding Time

secs

Test sequence 2 [29]: Encoding and Decoding time (secs) for Intra and Random access profiles

Basketball drill_Intra Basketball drill_Random Access0

50

100

150

200

250

300

350

400

Encoding Time vs Decoding Time

secs

Future WorkFuture simulations will be conducted using the HM 16.0 [16] software for other test sequences [29]; results will be plotted and compared for the best results using the parameters of PSNR, Bit rate, encoding and decoding time. Also BD-PSNR and BD-%bit rate reduction values will be plotted. Theoretical analysis will be carried out on sample-based angular prediction for HEVC lossless coding.

References[1] G.J. Sullivan et al, “Overview of the high efficiency video coding (HEVC) standard”, IEEE Trans. CSVT, vol. 22, pp.1649-1668, Dec. 2012.

[2] G.J. Sullivan et al, “Standardized Extensions of High Efficiency Video Coding (HEVC)”, IEEE Journal of selected topics in Signal Processing, vol.7, pp.1001-1016, Dec. 2013.

[3] C. Fogg, “Suggested figures for the HEVC specification”, ITU-T/ISO/IEC Joint Collaborative Team on Video Coding (JCT-VC) document JCTVC- J0292r1, July. 2012.

[4] M. Zhou et al, “HEVC lossless coding and improvements”, IEEE Trans. CSVT, vol.22, pp.1839-1843, Dec. 2013.

[5] N. Purnachand et al, "Fast Motion Estimation Algorithm for HEVC", IEEE Second International Conference on Consumer Electronics-Berlin (ICCE-Berlin), vol.11, pp.34-37, Sep. 2012.

[6] P. Hanhart et al, “ Subjective quality evaluation of the upcoming HEVC video compression standard”, SPIE Optical Engineering+ Applications, International Society for Optics and Photonics, vol. 8499, pp.84990v-84990v, Aug. 2012.

[7] M. Horowitz et al, “Informal subjective quality comparison of video compression performance of the HEVC and H.264/MPEG - 4 AVC standards for low delay applications”, SPIE Optical Engineering+ Applications, International Society for Optics and Photonics, vol.84990, pp.84990w-84990w, Aug. 2012.

[8] A. Abdelazim, W. Masri and B. Noaman., "Motion estimation optimization tools for the emerging high efficiency video coding (HEVC)", IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, vol. 9029, pp. 902905-902905, Feb. 2014.

[9] M. Jakubowski and G. Pastuszak, “Block-based motion estimation algorithms-a survey”, Journal of Opto-Electronics Review, vol. 21, pp.86-102, Mar. 2013.

[10] B. Bross et al, “High Efficiency Video Coding (HEVC) Text Specification Draft 10”, Document JCTVC-L1003, ITU-T/ISO/IEC Joint Collaborative Team on Video Coding (JCT-VC), Jan. 2013, available on http://phenix.it-sudparis.eu/jct/doc_end_user/current_document.php?id=7243

[11] J. Ascenso et al, "Improving Frame Interpolation with Spatial Motion Smoothing for Pixel Domain Distributed Video Coding", 5th EURASIP Conference on Speech and Image Processing, Multimedia Communications and Services, pp.1-6, Smolenice, Slovak Republic, July. 2005. 

[12] X. Wang et al, “Paralleling Variable Block Size Motion Estimation of HEVC on Multicore CPU plus GPU platform”, IEEE International Conference on Image Processing (ICIP), vol.22, pp. 1836-1839, Sep. 2013.

[13] Introduction to parallel computing https://computing.llnl.gov/tutorials/parallel_comp/#Whatis

[14] L. Zhao et al, “Group-Based Fast mode decision algorithm for intra prediction in HEVC”, IEEE Eighth international Conference on Signal Image Technology and Internet based Systems. Article no.6115979, pp. 225-229, Nov 2011.

[15] V. Sze and M. Budagavi, "High Throughput CABAC Entropy Coding in HEVC", IEEE Transactions on Circuits and Systems for Video Technology, vol.22, no.12, pp.1778-1791, Dec. 2012.

[16] T.Nguyen et al, "Transform Coding Techniques in HEVC", IEEE Journal of Selected Topics in Signal Processing, vol.7, pp.978–989, Dec. 2013.

[17] HEVC tutorial by I.E.G. Richardson: http://www.vcodex.com/h265.html

[18] HEVC Reference Software HM16.0. https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware/tags/HM-16.0rc1/

[19] B. Bross et al,“High Efficiency Video Coding (HEVC)Text Specification Draft 8”, JCT-VC document, JCTVC-J1003, Stockholm, Sweden, Jul. 2012.http://phenix.it-sudparis.eu/jct/doc_end_user/current_document.php?id=6465

[20] Joint Video Team, “Advanced Video Coding for Generic Audiovisual Services”, ITU-T Rec. H.264 and ISO/IEC, 14496-10 (MPEG-4) AVC, pp.H.100-H.869, Feb. 2014.

[21] Y.L. Lee et al, "Improved lossless intra coding for H.264/MPEG-4 AVC", IEEE Trans. Image Process., vol.15, no.9, pp.2610-2615, Sep. 2006. [22]K.R. Rao, D.N. Kim and J.J Hwang, “High Efficiency Video Coding (HEVC) Revised/Updated Chapter from the book Video Coding Standards”–Springer 2014.

[23] ITU-T website: http://www.itu.int/ITU-T/index.html

[24] JCT-VC documents are publicly available at http://ftp3.itu.ch/av-arch/jctvc-site and http://phenix.it-sudparis.eu/jct/

[25] V.Sze, M.Budagavi, and G.J. Sullivan “High Efficiency Video Coding (HEVC) Algorithms and architectures” Springer, 2014.

[26] Software reference manual for HM:

https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware/branches/HM-9.2-dev/doc/software-manual.pdf         

[27] M. Wien, “High efficiency video coding: Tools and specification”, Springer, 2015.

[28] I.E. Richardson, “Coding video: A practical guide to HEVC and beyond”, Wiley, 11 May. 2015

[29] Video Sequences:http://forum.doom9.org/archive/index.php/t-135034.htmlhttp://ultravideo.cs.tut.fi/

[30] C. Xun and Q. Gu, "Improved HEVC lossless compression using Two-Stage coding with Sub-Frame level optimal quantization values", IEEE International Conference. Image Processing (ICIP), vol.23, pp. 5651-5655, Oct. 2014.

[31] W. Eugen et al, "Pixel-based averaging predictor for HEVC lossless coding", IEEE International Conference. Image Processing (ICIP), vol.22, pp. 1806-1810, Sept. 2013.

[32] E. Wige et al, "In-loop denoising of reference frames for lossless coding of noisy image sequences" IEEE International Conference. Image Processing (ICIP), vol.19, pp. 461-464, Sept. 2010.

[33] A. Buades, B. Coll, and J.M. Morel, "A non-local algorithm for image denoising", IEEE Computer Society Conference. Computer Vision and Pattern Recognition (CVPR), vol.2, pp. 60-65, June. 2005.

[34] S.L. Yu and C. Chrysafis,” New intra prediction using intra-macroblock motion compensation”, Joint Video Team (JVT) of ISO/IEC MPEG &ITU-T VCEG (ISO/IEC JTC1/SC29/WG11 and ITU-T SG16 Std., doc. JVT-C151, Virginia, USA, Apr.2004.

[35] T.K. Tan, C. S. Boon, and Y. Suzuki, " Intra prediction by template matching ", IEEE International Conference. Image Processing (ICIP), vol.15, pp. 1693-1696, Oct. 2006.

[36] T.K. Tan et al, "Intra Prediction by Averaged Template Matching Predictors", IEEE Consumer Communications and Networking Conference (CCNC), vol.16, pp. 405-409, Jan 2007.

[37] V. Sze, M. Budagavi, “Design and Implementation of Next Generation Video Coding Systems (H.265/HEVC Tutorial),” IEEE International Symposium on Circuits and Systems (ISCAS), presented on June. 2014. http://www.rle.mit.edu/eems/publications/tutorials/