Adaptive deblocking filter

Adaptive deblocking filter
Adaptive deblocking filter

Adaptive Deblocking Filter

Peter List,Anthony Joch,Jani Lainema,Gisle Bj?ntegaard,and Marta Karczewicz

Abstract—This paper describes the adaptive deblocking filter used in the H.264/MPEG-4A VC video coding standard.The filter performs simple operations to detect and analyze artifacts on coded block boundaries and attenuates those by applying a selected filter.

Index Terms—Block-based coding,video coding,video filtering,video signal processing.

I.I NTRODUCTION

T

HERE are two building blocks within the architecture of the H.264/MPEG-4A VC video coding standard [1]which can be a source of blocking artifacts.The most significant one is the block-based integer discrete cosine transforms (DCTs)in intra and inter frame prediction error coding.Coarse quantiza-tion of the transform coefficients can cause visually disturbing discontinuities at the block boundaries [2]–[4].The second source of blocking artifacts is motion compensated prediction.Motion compensated blocks are generated by copying interpo-lated pixel data from different locations of possibly different reference frames.Since there is almost never a perfect fit for this data,discontinuities on the edges of the copied blocks of data typically arise.Additionally,in the copying process,existing edge discontinuities in reference frames are carried into the interior of the block to be compensated.Although the small

44sample transform size used in H.264/MPEG-4A VC somewhat reduces the problem,a deblocking filter is still an advantageous tool to maximize coding performance.

There are two main approaches in integrating deblocking fil-ters into video codecs.Deblocking filters can be used either as post filters or loop filters.Post filters only operate on the dis-play buffer outside of the coding loop,and thus are not norma-tive in the standardization process.Because their use is optional,post-filters offer maximum freedom for decoder implementa-tions.On the contrary,loop filters operate within the coding loop.That is,the filtered frames are used as reference frames for motion compensation of subsequent coded frames.This forces all standard conformant decoders to perform identical filtering in order to stay in synchronization with the encoder.Naturally,a decoder can still perform post filtering in addition to the loop filtering if found necessary in a specific application.

Manuscript received May 2,2003.

P.List is with Deutsche Telekom,T-Systems,64295Darmstadt,Germany (e-mail:Peter.List@https://www.360docs.net/doc/a75173285.html,).

A.Joch is with UB Video Inc.,Vancouver,BC V6B 2R9,Canada (e-mail:anthony@https://www.360docs.net/doc/a75173285.html,).

https://www.360docs.net/doc/a75173285.html,inema and M.Karczewicz are with the Nokia Research Center,Irving,TX 75039USA (e-mail:https://www.360docs.net/doc/a75173285.html,inema@https://www.360docs.net/doc/a75173285.html,;marta.kar-czewicz@https://www.360docs.net/doc/a75173285.html,).

G.Bj?ntegaard is with TANDBERG,N-1324Lysaker,Norway (e-mail:gbj@tandberg.no).

Digital Object Identifier 10.1109/TCSVT.2003.815175

Performing the filtering inside the coding loop has several advantages over post filtering.Firstly,the requirement of a loop filter guarantees a certain level of quality.This is especially im-portant in modern communications systems where decoders of several manufacturers are used to decode distributed video ma-terial.With a loop filter in the codec design,content providers can safely assume that their material is processed by proper de-blocking filters,guaranteeing the quality level expected by the producer.

Secondly,there is no need for an extra frame buffer in the decoder.In the post-filtering approach,the frame is typically decoded into a reference frame buffer.An additional frame buffer may be needed to store the filtered frame to be passed to the display device.In the loop-filtering approach,however,fil-tering can be carried out macroblock-wise during the decoding process,and the filtered output stored directly to the reference frame buffers.

Thirdly,empirical tests have shown that loop filtering typi-cally improves both objective and subjective quality of video streams with significant reduction in decoder complexity com-pared to post filtering [3],[5].Quality improvements are mainly due to the fact that filtered reference frames offer higher quality prediction for motion compensation.Reductions in computa-tional complexity can be achieved by taking into account the fact that the image area in past frames is already filtered,and thereby optimizing the filtering process accordingly.Fig.1shows the re-constructed frame in a loop-filter (left)and a post-filter (right)based TML 8.51system before loop/post filtering.It can be seen that the main coding artifact in the loop-filter case is the block-iness on the

4

(a)(b)

Fig.1.Detail of luminance input to the deblocking filter in the case of(a) loop-filtering and(b)post-filtering based system.

though the loop filter can be implemented without any multipli-cation or division operations.

The complexity is mainly based on the high adaptivity of the filter,which requires conditional processing on the block edge and sample levels.As a consequence,conditional branches al-most inevitably appear in the inner most loops of the algorithm. These are known to be very time consuming and are also quite a challenge for parallel processing in DSP hardware or SIMD code on general-purpose processors.

Another reason for the high complexity is the small block size employed for residual coding in the H.264coding algorithm. With the

4

8block structure.

In Sections II–IV,we provide an overview of the design of the H.264/MPEG-4A VC adaptive deblocking filter.For a complete and detailed description of the filtering process,see[1].Further information on the evolution of the filter design is available in the many contributions to the H.264/MPEG-4A VC standard-ization effort that influenced the final filter design,including [7]–[19].

II.B OUNDARY A NALYSIS

A.Error Distribution in a

4

4

block

4luminance sample blocks,a Boundary-Strength(Bs)parameter is assigned an integer value

from0to4.Table I shows how the value of Bs depends on

the modes and coding conditions of the two adjacent blocks.

In this table,conditions are evaluated from top to bottom,until

one of the conditions holds true,and the corresponding value is

assigned to Bs.

In the actual filtering algorithm,Bs determines the strength

of the filtering performed on the edge,including a selection

between the two primary filtering modes.A value of4means

a special mode of the filter is applied,which allows for the

strongest filtering,whereas a value of0means no filtering is

applied on this specific edge.In the standard mode of filtering

which is applied for edges with Bs from1to3,the value of

Bs affects the maximum modification of the sample values that

can be caused by filtering.The gradation of Bs reflects that the

strongest blocking artifacts are mainly due to intra and predic-

tion error coding and are to a somewhat smaller extent caused

by block motion compensation.

The Bs values for filtering of chrominance block edges are

not calculated independently,but instead copied from the values

calculated for their corresponding luminance edges.

In the case of macroblock adaptive frame/field coding

(MBAFF),the conditions in Table I get somewhat more com-

plex because any of the two adjacent blocks might belong to a

frame-or a field-coded macroblock.The principle of varying

filter strength remains the same in any case.To avoid excessive

blurring,special consideration is made to prevent very strong

Fig.2.One-dimensional visualization of a block edge in a typical situation where the filter would be turned on.

filtering of horizontal edges of field-coded macroblocks,since the spatial extent of the vertical filtering is doubled for such macroblocks.

C.Sample-Level Adaptivity of the Filter

In deblocking filtering,it is crucially important to be able to distinguish between true edges in the image and those created by quantization of the DCT coefficients.To preserve image sharp-ness,the true edges should be left unfiltered as much as possible while filtering artificial edges to reduce their visibility.

In order to separate these two cases,the sample values across every edge to be filtered are analyzed.Let us denote one line of

sample values inside two neighboring

4

,

,

,

and

(1)

are dependant on the average quantization parameter (QP)em-ployed over the edge,as well as encoder selected offset values that can be used to control the properties of the deblocking filter on the slice level.These table index values are calculated

as

and

(7)

Thus,in

general,

.To define the actual tables,variations from this basic relationship

have been made based on empirical tests to produce visually pleasing results for a variety of content.In particular,at the low end of the table,values are clipped to zero so that for values

of

and on QP links the strength of fil-tering to the general quality of the reconstructed picture prior to filtering.Since the thresholds values increase with QP,bound-aries that contain higher content activity are filtered when QP is larger,since the coding error (size of artifacts)increases with QP.The exponential nature

of

and

used in filtering and thereby increase or decrease the amount of filtering that takes place compared to filtering with the default zero offsets.The offset values are transmitted in the slice header syntax and are applied to the QP-based addressing of

the tables.

The ability to control the properties of deblocking filter by transmitting nonzero offsets provides the encoder designer with the ability to optimize the subjective quality of the decoded video beyond that provided by use of the default tables.For example,reducing the amount of filtering by transmitting neg-ative offsets can help to maintain the sharpness of small spa-tial details,particularly with high-resolution video content,in which small blocking artifacts tend to be less apparent.On the other hand,using positive offsets to increase the amount of fil-tering can improve subjective quality on content where visible blocking artifacts remain if the default values are used.This is beneficial for lower resolution content with smooth brightness transitions and to remove additional artifacts that might be in-troduced by sub-optimal motion estimation,mode decisions,or residual coding.

III.F ILTERING

A.Overview of Filtering Operations

In order to ensure a perfect match in the filtering process between encoders and decoders,filtering operations must be conducted in a specific order throughout each coded picture.Filtering is conducted “in-place,”so that the modified sample values after featuring each line of samples across an edge are used as input values to subsequent operations.

Filtering occurs on a macroblock basis,with horizontal fil-tering of the vertical edges performed first,followed by vertical filtering (of the horizontal edges).Both directions of filtering on each macroblock must be conducted before moving on to the next macroblock.The macroblocks are filtered in raster-scan order throughout the picture.For MBAFF coded frames,in which pairs of vertically adjacent macroblocks are grouped together,the filtering order is based on these macroblock pairs,with the pairs being filtered in raster-scan order throughout the frame,and the top macroblocks being filtered first within each pair.

For each luminance macroblock,the left-most edge of the macroblock is filtered first,followed from left to right by the three vertical edges that are internal to the macroblock.Simi-larly,the top edge of the macroblock is filtered first in the hor-izontal filtering pass,followed by the three internal horizontal edges from top to bottom.Chrominance filtering follows a sim-ilar order,with one external edge and one internal edge in each direction for each

8

,2,or 3).

For both filtering modes,

the

and

(10)

.

The values

of

are only modified if the corre-sponding condition (8)or (9)is true.Otherwise,the values are not modified.That is,if condition (8)is true,then the filtered value

of

value for

computing

is obtained accordingly,

substituting

,

and

values.This process is called clipping.Different procedures for clipping are applied for the interior and edge samples.

The

,

where used in

one dimension,and the Bs value used in the other.The value

of

,and then incremented by 1for each

of conditions (8)and (9)that holds true.Then,the amount of modification that will be applied to each of the edge samples is computed

as

(18)

Thus,stronger filtering is applied to the edge samples when the changes in intensity on each side of the edge are smaller than

the

sample values were also modified).

For chrominance filtering,only

the

values may be modified.These are filtered in the same way as for luminance,except that the clipping

value,

16

luma sample prediction modes when coding nearly uniform image areas.This causes small amplitude blocking artifacts at the macroblock boundaries.However,due to the Mach band effect [20],even very small differences in the intensity values are perceived as abrupt steps in these cases.To compensate for this tiling effect,stronger filtering is applied on boundaries between two macroblocks with smooth image content.

For luminance filtering,a decision is made based on the image content between a very strong 4-and 5-tap filter that modifies the edge sample and two interior samples on each side,or a weaker 3-tap filter modifies only to the edge sample.The stronger filter is only applied when the following constraint on the difference across the edge holds

true:

TABLE II

A VERAGE

B IT R ATE S A VINGS FOR A LIGNED PSNR S O BTAINED W ITH

TML8.5L OOP F ILTER

Fig.3.Luminance PSNR curve for Foreman with and without a loop filter in TML8.5.

Notice that(19)is a similar condition to(1),with a tighter con-straint on the maximum sample value difference across the edge. For luminance filtering,when the smoothness conditions(8) and(19)hold true,the filtered values are calculated according to the following equations:

and

(a)(b)

Fig.7.Detail of the luminance output in the case of (a)loop filtering and (b)no filtering.CIF sequence was coded at 200kbps and 15

fps.

(a)(b)

Fig.8.Detail of the luminance output in the case of (a)loop filtering and (b)no filtering.QCIF sequence was coded at 30kbps and 10fps.

provements with a reasonably simple algorithm.The good performance is based on reliable detection of real and artifi-cially created edges and efficient filtering of the latter ones.Bit-rate savings exceeding 9%are observed with equal PSNR levels together with significantly improved visual quality.

R EFERENCES

[1]Draft ITU-T Recommendation and Final Draft International Standard of

Joint Video Specification (ITU-T Rec.H.264/ISO/IEC 14496-10A VC),Mar.2003.

[2]K.K.Pang and T.K.Tan,“Optimum loop filter in hybrid coders,”IEEE

Trans.Circuits Syst.Video Technol.,vol.4,pp.158–167,Apr.1994.[3]Y .-L.Lee and H.W.Park,“Loop filtering and post-filtering for low-bit-rates moving picture coding,”Signal Processing:Image Commun.,vol.16,pp.871–890,2001.

[4]S.D.Kim,J.Yi,H.M.Kim,and J.B.Ra,“A deblocking filter with

two separate modes in block-based video coding,”IEEE Trans.Circuits Syst.Video Technol.,vol.9,pp.156–160,Feb.1999.

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[8]

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[9]G.Bj?ntegaard and I.Lille-Langoy,“Possible Simplifications of the

Present Deblocking Filter in TML 5.9,”ITU-T SG16Doc.VCEG-M30,2001.

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[12]S.Sun and S.Lei,“Improved TML Loop Filter With Lower Com-plexity,”ITU-T SG16Doc.VCEG-N17,2001.

[13]G.C?té,L.Winger,and M.Gallant,“Lower Complexity Deblocking

Filter With In-Place Filtering,”Doc.VCEG-O39,2001.

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[15]J.Au,B.Lin,A.Joch,and F.Kossentini,“Complexity Reduction and

Analysis for Deblocking Filter,”Doc.JVT-C094,2002.

[16] A.Joch,“Improved Loop-Filter Tables and Variable-Shift Table In-dexing,”Doc.JVT-D038,2002.[17],“Loop Filter Simplification and Improvement,”Doc.JVT-D037,

2002.

[18] C.Gomila and A.Joch,“Simplified Chroma Deblocking (Revisited),”

Doc.JVT-E089,2002.

[19] A.MacInnis and S.Zhong,“Corrections to Loop Filter in the Case of

MB-AFF,”Doc.JVT-F027,2002.

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Peter List was born in 1957.He graduated in physics in 1985and received the Ph.D.degree in applied physics in 1989,both from the University of Frankfurt/Main,Germany.

He is project manager at T-Systems Nova,Darmstadt,Germany,a reserach and development company of Deutsche Telekom.Since 1990,he has been with Deutsche Telekom,and has actively followed the international standardization of video compression in ISO,ITU,and several European projects for more than ten

years.

Anthony Joch received the B.Eng.degree in computer engineering from McMaster University,Hamilton,ON,Canada,in 1999,and the M.A.Sc.degree in electrical engineering from the University of British Columbia,V ancouver,BC,Canada,in 2002.

In 2000,he joined UB Video Inc.,Vancouver,BC,where he is currently a Senior Engineer involved in the development of software codecs for the H.264/MPEG-4A VC standard.His research interests include reduced-complexity algorithms for

video encoding,video pre-and post-processing,and multimedia systems.He has been an active contributor to the H.264/MPEG-4A VC standardization effort,particularly in the area of deblocking filtering and as a co-chair of the ad-hoc group for bitstream exchange.

Jani Lainema received the M.Sc.degree in computer engineering from Tam-pere University of Technology,Tampere,Finland,in 1996.

He joined the Visual Communications Laboratory of Nokia Research Center,Irving,TX,in 1996,where he is currently a Project Manager and Senior Re-search Scientist.His research interests include video,image and graphics coding and communications.

Gisle Bj?ntegaard received the Dr.Phil.degree in physics from the University of Oslo,Oslo,Norway,in 1974.

From 1974to 1996,he was a Senior Scientist with Telenor Research and Development,Oslo,Norway.His areas of research included radio link network design,reflector antenna design and construction,digital signal procession,and development of video compression methods.From 1996to 2002,he was a Group Manager at Telenor Broadband Services,Oslo,Norway,where his areas of work included the design of point-to-point satellite communication and development of satellite digital TV platform.Since 2002,he has been a Principal Scientist at Tandberg Telecom,Lysaker,Norway,working with video-coding development and implementation.He has contributed actively to the development of the ITU video standards H.261,H.262,H.263,and H.264,as well as to ISO/IEC MPEG2and MPEG4.

Marta Karczewicz received the M.S.degree in electrical engineering in 1994and Dr.Technol.degree in 1997,both from Tampere University of Technology,Tampere,Finland.

During 1994–1996,she was Researcher in the Signal Processing Laboratory,Tampere University of Technology.Since 1996,she has been with the Visual Communication Laboratory,Nokia Research Center,Iriving,TX,where she is currently a Senior Research Manager.Her research interests include image com-pression,communication and computer graphics.

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(4).可扩展性:买一台性能更高的大型机,或者再买一台性能相同的大型机的费用都比添加几台PC的费用高得多。 (5).高度灵活性:能够兼容不同硬件厂商的产品,兼容低配置机器和外设而获得高性能计算。 (6).高度自治性。通过自动化配置管理服务,可以按需自动调配服务,以及根据自动增加/减少服务的数量。 云计算具有以下特点: (1) 超大规模:Google云计算已经拥有100多万台服务器, Amazon、IBM、微软、Yahoo等的“云”均拥有几十万台服务器。企业私有云一般拥有数百上千台服务器。“云”能赋予用户前所未有的计算能力。 (2) 虚拟化。云计算支持用户在任意位置、使用各种终端获取应用服务。所请求的资源来自“云”,而不是固定的有形的实体。应用在“云”中某处运行,但实际上用户无需了解、也不用担心应用运行的具体位置。只需要一台笔记本或者一个手机,就可以通过网络服务来实现我们需要的一切,甚至包括超级计算这样的任务。 (3) 高可靠性。“云”使用了数据多副本容错、计算节点同构可互换等措施来保障服务的高可靠性,使用云计算比使用本地计算机可靠。 (4) 通用性。云计算不针对特定的应用,在“云”的支撑下可以构造出千变万化的应用,同一个“云”可以同时支撑不同的应用运行。 (5) 高可扩展性。“云”的规模可以动态伸缩,满足应用和用户规模增长的需要。 (6) 按需服务。“云”是一个庞大的资源池,你按需购买;云可以象自来水,电,煤气那样计费。 (7) 极其廉价。由于“云”的特殊容错措施可以采用极其廉价的节点来构成云,“云”的自动化集中式管理使大量企业无需负担日益高昂的数据中心管理成本,“云”的通用性使资源的利用率较之传统系统大幅提升,因此用户可以充分享受“云”的低成本优势,经常只要花费几百美元、几天时间就能完成以前需要数万美元、数月时间才能完成的任务。 (8) 潜在的危险性:云计算服务当前垄断在私人机构(企业)手中,而他们

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