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ByAmruta Kulkarni

Under Guidance of DR. K.R. RAO Fast mode decision for Inter Mode Selection in H.264/AVC Video CodingContentsNeed for video compressionMotivationVideo coding standards, video formats and qualityOverview of H.264Complexity reduction algorithm for inter mode selectionExperimental resultsConclusionsReferences

Need for Video CompressionIt reduces both storage and bandwidth demands. Insufficient resources to handle uncompressed videos.Better proposition is to send high-resolution compressed video than a low-resolution, uncompressed stream over a high bit-rate transmission channel.

Motivation [2]Removing redundancy in a video clipOnly a small percentage of any particular frame is new informationHighly complex process

Reduce the overall complexity suitable for handheld devices Timeline of Video Development [10]Inter-operability between encoders and decoders from different manufacturersBuild a video platform which helps to interact with video codecs, audio codecs, transport protocols, security and rights management in well defined and consistent ways

OVERVIEW OF H.264 / AVC STANDARD

Built on the concepts of earlier standards such as MPEG-2 and MPEG-4 Visual Achieves substantially higher video compression and has network friendly video representation50% reduction in bit-rate over MPEG-2Error resilience toolsSupports various interactive (video telephony) and non-interactive applications (broadcast, streaming, storage, video on demand)H.264/MPEG-4 Part 10 or AVC [2, 5]Is an advanced video compression standard, developed by ITU-T Video Coding Experts Group(VCEG) together with ISO/IEC Moving Picture Experts Group(MPEG). It is a widely used video codec in mobile applications, internet ( YouTube, flash players), set top box, DTV etc. A H.264 encoder converts the video into a compressed format(.264) and a decoder converts compressed video back into the original format.

How does H.264 codec work ?An H.264 video encoder carries out prediction, transform and encoding processes to produce a compressed H.264 bit stream. The block diagram of the H.264 video encoder is shown in Fig 1. A decoder carries out a complementary process by decoding, inverse transform and reconstruction to output a decoded video sequence. The block diagram of the H.264 video decoder is shown in Fig 2.

H.264 encoder block diagram

Fig. 1 H.264 Encoder block diagram[7] H.264 decoder block diagramMotionCompensationEntropyDecodingIntraPredictionIntra/Inter ModeSelectionInverse Quantization& Inverse TransformDeblockingFilter++Bitstream InputVideoOutputPictureBufferingFig.2 H.264 decoder block diagram [2]Slice Types [3]I (intra) slice contains reference only to itself.P (predictive) slice uses one or more recently decoded slices as a reference (or prediction) for picture construction.B (bi-predictive) slice works similar to P slices except that former and future I or P slices may be used as reference picturesSI and SP or switching slices may be used for transitions between two different H.264 video streams.

Profiles in H.264The H.264 standard defines sets of capabilities, which are also referred to as Profiles, targeting specific classes of applications. Fig. 3. Different features are supported in different profiles depending on applications. Table 1. lists some profiles and there applications. ProfileApplicationsBaselineVideo conferencing , VideophoneMain Digital Storage Media, Television BroadcastingHigh Streaming Video ExtendedContent distribution Post processingTable 1. List of H.264 Profiles and applications[2]Profiles in H.264[9]

Fig. 3 Profiles in H. 264[9]Intra PredictionI pictures usually have a large amount of information present in the frame.The spatial correlation between adjacent macro-blocks in a given frame is exploited.H.264 offers nine modes for intra prediction of 4x4 luminance blocks.H.264 offers four modes of intra prediction for 16x16 luminance block.H.264 supports four modes similar to 16x16 luminance block for prediction of 8x8 chrominance blocks.

Fig.4 16x16 intra prediction modes [11]Fig. 5 4x4 Intra prediction modes [11]Intra predictionInter Prediction [5]Takes advantage of the temporal redundancies that exist among successive frames.Temporal prediction in P frames involves predicting from one or more past frames known as reference frames.

Motion Estimation/CompensationIt includes motion estimation (ME) and motion compensation (MC).ME/MC performs prediction. A predicted version of a rectangular block of pixels is generated by choosing another similarly sized rectangular block of pixels from previously decoded reference picture.Reference block is translated to the position of current rectangular block (motion vector).Different sizes of block for luma: 4x4, 4x8, 8x4, 8x8, 16x8, 8x16, 16x16 pixels.Inter predictionFig. 6 Partitioning of a MB for motion compensation [5]

Integer Transform and QuantizationTransform:Prediction error block is expressed in the form of transform co-efficients. H.264 employs a purely integer spatial transform, which is a rough approximation of the DCT.Quantization:Significant portion of data compression takes place.Fifty-two different quantization step sizes can be chosen.Step sizes are increased at a compounding rate of approximately 12.5%.

De-blocking Filter and Entropy CodingDe-blocking filter:Removes the blocking artifacts due to the block based encoding patternIn-loop de-blocking filter

Entropy coding:Assigning shorter code-words to symbols with higher probabilities of occurrence, and longer code-words to symbols with less frequent occurrences.CAVLC and CABACFAT (Fast Adaptive Termination) for Mode Selection [9]The proposed fast adaptive mode selection algorithm includes the following:Fast mode predictionAdaptive rate distortion thresholdHomogeneity detectionEarly Skip mode detection

Fast mode predictionIn H264/ AVC video coding is performed on each frame by dividing the frame into small macro blocks from up-left to right-bottom direction. The spatial macro blocks in the same frame generally have the similar characteristics such as motion, detailed region. For example, if most of the neighboring macro blocks have skip mode, that means the current macro block has more chance of having the same mode. Temporal similarity also exists between the collocated macro blocks in the previous encoded frame.

Fast mode predictionFig. 7 shows the spatial macro blocks, the current macro block X has similar characteristics with its neighboring macro blocks from A through H. In Fig. 8 shows the temporal similarity between current and collocated macro block PX in the previous frame and its neighbors.

Fig. 7 Spatial Neighboring blocks [8]Fig. 8 Temporal Neighboring blocks [8]23Fast mode prediction A mode histogram from spatial and temporal neighboring macro blocks is obtained, we select the best mode as the index corresponding to the maximum value in the mode histogram. The average rate-distortion cost of each neighboring macro block corresponding to the best mode is then selected as the prediction cost for the current macro block.

Rate Distortion OptimizationRatedistortion optimization (RDO) is a method of improving video quality in video compression. The name refers to the optimization of the amount of distortion (loss of video quality) against the amount of data required to encode the video, the rate. Macro block parameters : QP(quantization parameter) and Lagrange multiplier ()Calculate : Mode= 0.85*2(QP-12)/3Then calculate cost, which determines the best mode,

RDcost = D + MODE * R, D Distortion R - bit rate with given QP Lagrange multiplier

Distortion (D) is obtained by SAD (Sum of Absolute Differences) between the original macro block and its reconstructed block. Bit rate(R) includes the bits for the mode information and transform coefficients for macro block . Quantization parameter (QP) can vary from (0-51)Lagrange multiplier () a value representing the relationship between bit cost & quality. Adaptive Rate Distortion ThresholdRDthres for early termination is dependent on RD pred which is computed according to spatial and temporal correlations. RDthres also depends on the value of modulator. Thus, rate distortion threshold is given by, Rdthres = (1+ ) x RD pred

modulator provides a trade-off between computational efficiency and accuracy.

Threshold selectionAdaptive Threshold I: RD thres = RD pred x (1-8x)Adaptive Threshold II: RD thres = RD pred x (1+10x)The threshold is adaptive as it depends on the predicted rate distortion cost derived from spatial and temporal correlations. Where, is the modulation Coefficient, and it depends on two factors namely quantization step (Qstep) and block size (N and M).

Homogeneity Detection Smaller block sizes like P4x8, P8x4 and P4x4 often correspond to detailed regions and thus requires much more computation when compared to larger block sizes. So, before checking smaller block sizes it is necessary to check if a P8x8 block is homogeneous or not. The method adopted to detect homogeneity is based on edge detection. An edge map is created for each frame using the Sobel operator [27]. Homogeneity Detection For each pixel pm, n, an edge vector is obtained Dm,n ( dxm,n, dym,n) dxm, n = pm-1, n+1 + 2 * pm, n+1 + pm+1, n+1 - pm-1, n-1 2 * pm, n- 1 - pm+1, n-1 (1)dym,n = pm+1, n-1 + 2 * pm+1, n + pm+1, n+1 - pm-1, n-1 2 * pm-1, n - pm-1, n+1 (2)Here dxm, n and dym, n represent the differences in the vertical and horizontal directions respectively. The amplitude Amp (D (m, n)) of the edge vector is given by, Amp (D (m, n)) = dxm, n + dym, n (3)A homogeneous region is detected by comparing the summation of the amplitudes of edge vectors over one region with predefined threshold values [30]. In the proposed algorithm, such thresholds are made adaptive depending on the amplitude of left, up blocks and mode information.

Homogeneity DetectionThe adaptive threshold is determined as per the following four cases: Case 1: If the left block and the up block are both P8x8

Case 2: If the left block is P8x8 and up block is not P8x8 Threshold =

Homogeneity Detection Case 3: If the left block is not P8x8 and up block is P8x8Threshold =

Case 4: If the left block is not P8x8 and up block is not P8x8

FAT Algorithm [8]

Fig. 9 FAT algorithm [8]

FAT AlgorithmStep 1 : If current macro block belongs to I slice, check for intra prediction using I4x4 or I16x16,go to step 10 else go to step 2. Step 2 : If a current macro block belongs to the first macro block in P slice check for inter and intra prediction modes, go to step 10 else go to step 2. Step 3: Compute mode histogram from neighboring spatial and temporal macro blocks, go to step 4. Step 4 : Select prediction mode as the index corresponding to maximum in the mode histogram and obtain values of Adaptive Threshold I and Adaptive Threshold II, go to step 5. Step 5 : Always check over P16x16 mode and check the conditions in the skip mode, if the conditions of skip mode are satisfied go to step 10, otherwise go to step 6. FAT Algorithm Step 6 : If all left, up , up-left and up-right have skip modes, then check the skip mode against Adaptive Threshold I if the rate distortion is less than Adaptive Threshold I , the current macro block is labeled as skip mode and go to step 10, otherwise, go to step 7.Step 7 : First round check over the predicted mode; if the predicted mode is P8x8, go to step 8; otherwise, check the rate distortion cost of the predicted mode against Adaptive Threshold I. If the RD cost is less than Adaptive Threshold I, go to step 10; otherwise go to step 9. Step 8 : If a current P8x8 is homogeneous, no further partition is required. Otherwise, further partitioning into smaller blocks 8x4,4x8, 4x4 is performed. If the RD of P8x8 is less than Adaptive Threshold I , go to step 10; otherwise go to step 9. FAT AlgorithmStep 9 : Second round check over the remaining modes against Adaptive Threshold II : If the rate distortion is less than Adaptive Threshold II; go to step 10; otherwise continue check all the remaining modes, go to step 10.Step 10 : Save the best mode and rate distortion cost. CIF and QCIF sequencesCIF (Common Intermediate Format) is a format used to standardize the horizontal and vertical resolutions in pixels of Y, Cb, Cr sequences in video signals, commonly used in video teleconferencing systems.QCIF means "Quarter CIF". To have one fourth of the area as "quarter" implies the height and width of the frame are halved.The differences in Y, Cb, Cr of CIF and QCIF are as shown below in fig.6. [16]

Fig.10 CIF and QCIF resolutions(Y, Cb, Cr ). ResultsThe following QCIF and CIF sequences were used to test the complexity reduction algorithm. [10]Akiyo ForemanCar phoneHall monitorSilentNewsContainerCoastguard

Test Sequences

NewsForemanAkiyoCoastguard

Car phoneContainer

Test Sequences

Hall monitorSilentExperimental ResultsBaseline profileIPPP type.Various QP of 22,27, 32 and 37.QCIF -30 frames CIF - 30 framesThe results were compared with exhaustive search of JM in terms of the change of PSNR, bit-rate, SSIM, compression ratio, and encoding time.Intel Pentium Dual Core processor of 2.10GHz and 4GB memory.Experimental ResultsComputational efficiency is measured by the amount of time reduction, which is computed as follows:

Delta Bit rate is measured by the amount of reduction which is computed by,

Delta PSNR (Peak Signal to Noise Ratio) is measured by the amount of reduction which is computed by,

QualitySpecify, evaluate and compareVisual quality is inherently subjective. Two types of quality measures : Objective quality measure- PSNR, MSE Structural quality measure- SSIM [29]PSNR - most widely used objective quality measurementPSNRdB = 10 log10 ((2n 1)2 / MSE)where, n = number of bits per pixel, MSE = mean square errorSSIM SSIM emphasizes that the human visual system is highly adapted to extract structural information from visual scenes. Therefore, structural similarity measurement should provide a good approximation to perceptual image quality.

Results

ConclusionsTo achieve time complexity reduction in inter prediction, a fast adaptive termination mode selection algorithm, named FAT [8] has been used.Experimental results reported on different video sequences and comparison with open source code (JM17.2) indicate that the algorithm used achieves faster encoding time with a negligible loss in video quality. Numbers are as shown below:Encoding time: ~43% reduction for QCIF and ~40% reduction for CIFPSNR: ~0.15% reduction for QCIF and ~0.26% reduction for CIFBit Rate: ~6% reduction for QCIF and ~9.5% reduction for CIFSSIM: ~0.077% reduction for QCIF and ~0.073% reduction for CIFThese results show that considerable reduction in encoding time is achieved using FAT algorithm while not degrading the video qualityReferences: Open source article, Intra frame coding : http://www.cs.cf.ac.uk/Dave/Multimedia/node248.htmlOpen source article, MPEG 4 new compression techniques : http://www.meabi.com/wp-content/uploads/2010/11/21.jpgOpen source article, H.264/MPEG-4 AVC, Wikipedia Foundation, http://en.wikipedia.org/wiki/H.264/MPEG-4_AVCI.E.Richardson, The H.264 advanced video compression standard,2nd Edition ,Wiley 2010. R. Schafer and T. Sikora, Digital video coding standards and their role in video communications, Proceedings of the IEEE Vol 83,pp. 907-923,Jan 1995.G. Escribano et al, Video encoding and transcoding using machine learning, MDM/KDD08,August 24,2008,Las Vegas,NV,USA. D. Marpe, T. Wiegand and S. Gordon, H.264/MPEG4-AVC Fidelity Range Extensions: Tools, Profiles, Performance, and Application Areas, Proceedings of the IEEE International Conference on Image Processing 2005, vol. 1, pp. 593 - 596, Sept. 2005. ITU-T Recommendation H.264-Advanced Video Coding for Generic Audio-Visual services.

S. Kwon, A. Tamhankar and K.R. Rao, Overview of H.264 / MPEG-4 Part 10, J. Visual Communication and Image Representation, vol. 17, pp.186-216, April 2006.A. Puri et al, Video coding using the H.264/ MPEG-4 AVC compression standard, Signal Processing: Image Communication, vol. 19, pp: 793 849, Oct. 2004.G. Sullivan, P. Topiwala and A. Luthra, The H.264/AVC Advanced Video Coding Standard: Overview and Introduction to the Fidelity Range Extensions, SPIE conference on Applications of Digital Image Processing XXVII, vol. 5558, pp. 53-74, Aug. 2004.K. R. Rao and P. C. Yip, The transform and data compression handbook, Boca Raton, FL: CRC press, 2001.T. Wiegand and G. J. Sullivan, The H.264 video coding standard, IEEE Signal Processing Magazine, vol. 24, pp. 148-153, March 2007.I.E.Richardson H.264/MPEG-4 Part 10 White Paper : Inter Prediction, www.vcodex.com, March 2003.JM reference software http://iphome.hhi.de/suehring/tml/G. Raja and M.Mirza, In-loop de-blocking filter for H.264/AVC Video, Proceedings of the IEEE International Conference on Communication and Signal Processing 2006, Marrakech, Morroco, Mar. 2006.M. Wien, Variable block size transforms for H.264/AVC, IEEE Trans. on Circuits and Systems for Video Technology, vol. 13, pp. 604613, July 2003.A. Luthra, G. Sullivan and T. Wiegand, Introduction to the special issue on the H.264/AVC video coding standard, IEEE Trans. on Circuits and Systems for Video Technology, vol. 13, issue 7, pp. 557-559, July 2003.

A. Luthra, G. Sullivan and T. Wiegand, Introduction to the special issue on the H.264/AVC video coding standard, IEEE Trans. on Circuits and Systems for Video Technology, vol. 13, issue 7, pp. 557-559, July 2003. H.Kim and Y.Altunhasak, Low-Complexity macroblock mode selection for H.264-AVC encoders, IEEE International Conference on Image Processing, vol.2, pp .765-768, Oct. 2004.Editor's Proposed Draft Text Modifications for Joint Video Specification (ITU-T Rec. H.264 | ISO/IEC 14496-10 AVC), Draft 2, JVT-E022d2, Geneva, Switzerland, 9-17 October, 2002A. Tourapis, O. C. Au, and M. L. Liou, Highly efficient predictive zonal algorithm for fast block-matching motion estimation, IEEE Trans. Circuits Syst. Video Technol., vol. 12, pp. 934-947, Oct. 2002 Z. Chen, P. Zhou and Y He, Fast integer pel and fractional pel motion estimation for JVT, JVT-F017r1.doc, Joint Video Team (JVT) of ISO/IEC MPEG & ITU-T VCEG, 6th meeting, Awaji, Island, JP, 5-13 December, 2002.A. M. Tourapis, " Enhanced predictive zonal search for single and multiple frame motion estimation, in proceedings of Visual Communications and Image Processing 2002 (VCIP-2002), pp. 1069-1079, San Jose, CA, January 2002.Y. Lin and S. Tai, Fast full search block matching algorithm for motion compensated video compression, IEEE Transactions on Communications, vol. 45, pp. 527-531, May 1997.T. Uchiyama, N. Mukawa, and H.Kaneko, Estimation of homogeneous regions for segmentation of textured images, Proceedings of IEEE ICPR, pp. 1072-1075, 2002. X. Liu, D. Liang and A. Srivastava, Image segmentation using local special histograms, Proceedings of IEEE ICIP, pp. 70-73, 2001. F. Pan, X. Lin, R. Susanto, K. Lim,Z. Li,G. Feng,D. Wu and S. Wu, Fast mode decision for intra prediction, Doc. JVT-G013,Mar.2003.

D. Wu et al ,Fast intermode decision in H.264/AVC video coding, IEEE Transactions on Circuits and System for Video Technology, vol. 15, no. 7, pp. 953-958,July 2005. YUV test video sequences : http://trace.eas.asu.edu/yuv/J. Ren et al , Computationally efficient mode selection in H.264/AVC video coding, IEEE Transactions on Consumer Electronics, Vol. 54, No.2, pp. 877-886, May 2008. Z. Wang et al,Image quality assessment: From error visibility to structural similarity, IEEE Transactions on Image Processing, vol.13, no.4, pp.600-612, Apr. 2004. A.Puri et al, Video coding using the H.264/MPEG-4 AVC compression standard, Signal Processing: Image Communication, vol.19, pp. 793-849, Oct. 2004.Multi-view Coding H.264/MPEG-4 AVC : http://mpeg.chiariglione.org/technologies/mpeg-4/mp04-mvc/index.htm CIF and QCIF format : http://en.wikipedia.org/wiki/Common_Intermediate_FormatT.Wiegand et al,Rate-constrained coder control and comparison of video coding standards, IEEE Trans. Circuits Systems Video Technology, vol. 13, no.7, pp.688-703, July 2003.T.Stockhammer, D.Kontopodis, and T.Wiegand, Rate-distortion optimization for H.26L video coding in packet loss environment, in Proc. Packet Video Workshop 2002, Pittsburgh, PA, April 2002. K.R.Rao and J.J.Hwang,Techniques and standards for digital image/video/audio coding, Englewood Cliffs, NJ: Prentice Hall, 1996.Open source article,Blu-ray discs, http://www.blu-ray.com/info/Open source article, Coding of moving pictures and audio ,http://mpeg.chiariglione.org/standards/mpeg-2/mpeg-2.htmOpen source article, Studio encoding parameters of digital television for standard 4:3 and wide screen 16:9 aspect ratios, http://www.itu.int/rec/R-REC-BT.601/Integrated Performance Primitives from Intel Website: http://software.intel.com/en-us/articles/intel-ipp/#support, 2009.T.Purushotham, Low complexity H.264 encoder using machine learning, M.S. Thesis, E.E Dept, UTA, 2010. S.Muniyappa, Implementation of complexity reduction algorithm for intra mode selection in H.264/AVC, M.S. Thesis, E.E Dept, UTA, 2011.R.Su, G.Liu and T.Zhang, Fast mode decision algorithm for intra prediction in H.264/AVC with integer transform and adaptive threshold, Signal, Image and Video Processing, vol.1, no.1, pp. 11-27, Apr. 2007.D.Kim, K.Han and Y.Lee, Adaptive single-multiple prediction for H.264/AVC intra coding, IEEE Trans. on Circuits and Systems for Video Technology, vol. 20, no. 4, pp. 610-615, April 2010. G.J.Sullivan, The H.264/ MPEG-4 AVC video coding standard and its deployment status, Proc. SPIE Conf. Visual Communications and Image Processing (VCIP), Beijing, China, July 2005. D.Marpe, T.Wiegand and G.Sullivan, The H.264/MPEG-4 advanced video coding standard and its applications, IEEE Communications Magazine, vol. 44, no.8, pp. 134-143, Aug. 2006. T.Wiegand and G.Sullivan,The picturephone is here: Really, IEEE Spectrum, vol.48, pp.50-54, Sept. 2011.

Thank You !! SSIM The difference with respect to other techniques mentioned previously such as MSE or PSNR, is that these approaches estimate perceived errors on the other hand SSIM considers image degradation as perceived change in structural information. Structural information is the idea that the pixels have strong inter-dependencies especially when they are spatially close. These dependencies carry important information about the structure of the objects in the visual scene.The SSIM metric is calculated on various windows of an image. The measure between two windows and of common size NN is:With the average of x ;the average of y;the variance of x;the variance of y ;the covariance of and xy;C1 and C2, two variables to stabilize the division with weak denominator;In order to evaluate the image quality this formula is applied only on luma. The resultant SSIM index is a decimal value between -1 and 1, and value 1 is only reachable in the case of two identical sets of data. Typically it is calculated on window sizes of 88. The window can be displaced pixel-by-pixel on the image but the authors propose to use only a subgroup of the possible windows to reduce the complexity of the calculation.

RDO Ratedistortion optimization (RDO) is a method of improving video quality in video compression. The name refers to the optimization of the amount of distortion (loss of video quality) against the amount of data required to encode the video, the rate. While it is primarily used by video encoders, rate-distortion optimization can be used to improve quality in any encoding situation (image, video, audio, or otherwise) where decisions have to be made that affect both file size and quality simultaneously. Ratedistortion optimization solves the aforementioned problem by acting as a video quality metric, measuring both the deviation from the source material and the bit cost for each possible decision outcome. The bits are mathematically measured by multiplying the bit cost by the Lagrangian, a value representing the relationship between bit cost and quality for a particular quality level. The deviation from the source is usually measured as the mean squared error, in order to maximize the PSNR video quality metric.Calculating the bit cost is made more difficult by the entropy encoders in modern video codecs, requiring the rate-distortion optimization algorithm to pass each block of video to be tested to the entropy coder to measure its actual bit cost. In MPEG codecs, the full process consists of a discrete cosine transform, followed by quantization and entropy encoding. Because of this, rate-distortion optimization is much slower than most other block-matching metrics, such as the simple sum of absolute differences (SAD) and sum of absolute transformed differences (SATD). As such it is usually used only for the final steps of the motion estimation process, such as deciding between different partition types in H.264/AVC.

PSNR The PSNR is most commonly used as a measure of quality of reconstruction of lossy compression codecs (e.g., for image compression). The signal in this case is the original data, and the noise is the error introduced by compression. When comparing compression codecs it is used as an approximation to human perception of reconstruction quality, therefore in some cases one reconstruction may appear to be closer to the original than another, even though it has a lower PSNR (a higher PSNR would normally indicate that the reconstruction is of higher quality). One has to be extremely careful with the range of validity of this metric; it is only conclusively valid when it is used to compare results from the same codec (or codec type) and same content.[1][2]It is most easily defined via the mean squared error (MSE) which for two mn monochrome images I and K where one of the images is considered a noisy approximation of the other is defined as:The PSNR is defined as:Here, MAXI is the maximum possible pixel value of the image. When the pixels are represented using 8 bits per sample, this is 255. More generally, when samples are represented using linear PCM with B bits per sample, MAXI is 2B1. For color images with three RGB values per pixel, the definition of PSNR is the same except the MSE is the sum over all squared value differences divided by image size and by three. Alternately, for color images the image is converted to a different color space and PSNR is reported against each channel of that color space, e.g., YCbCr or HSL.[3][4][5]Typical values for the PSNR in lossy image and video compression are between 30 and 50dB, where higher is better.[6][7] Acceptable values for wireless transmission quality loss are considered to be about 20dB to 25dB.[8][9]When the two images are identical, the MSE will be zero. For this value the PSNR is undefined (

Bit rateIn telecommunications and computing, bit rate (sometimes written bitrate, data rate or as a variable R[1]) is the number of bits that are conveyed or processed per unit of time.In digital multimedia, bitrate represents the amount of information, or detail, that is stored per unit of time of a recording. The bitrate depends on several factors:The original material may be sampled at different frequenciesThe samples may use different numbers of bitsThe data may be encoded by different schemesThe information may be digitally compressed by different algorithms or to different degreesGenerally, choices are made about the above factors in order to achieve the desired trade-off between minimizing the bitrate and maximizing the quality of the material when it is played.If lossy data compression is used on audio or visual data, differences from the original signal will be introduced; if the compression is substantial, or lossy data is decompressed and recompressed, this may become noticeable in the form of compression artifacts. Whether these affect the perceived quality, and if so how much, depends on the compression scheme, encoder power, the characteristics of the input data, the listeners perceptions, the listener's familiarity with artifacts, and the listening or viewing environment.

The most computational expensive process in H.264 is the Motion Estimation.For example, assuming FS and P block types, Q reference frames and a search range of MxN, MxNxPxQ computions are needed.