High-definition Video Coding with Super-macroblocks

High-definition Video Coding with Super-macroblocks Siwei Ma and C.-C. Jay Kuo Ming-Hsieh Department of Electrical Engineering and Integrated Media Systems Center University of Southern California, Los Angeles, CA 90089-2564, USA ABSTRACT A high definition video coding technique using super-macroblocks is investigated in this work. Our research is motivated by the observation that the macroblock-based partition in H.264/AVC may not be efficient for high definition video since the maximum macroblock size of 16 × 16 is relatively small against the whole image size. In the proposed super-macboblock based video coding scheme, the original block size M × N in H.264 is scaled to 2M × 2N. Along with the super-macroblock prediction framework, a low-complexity 16 × 16 discrete cosine transform (DCT) is proposed. As compared with the 1D 8 × 8 DCT, only 16 additions are added for a 1D 16 points 16 × 16 DCT. Furthermore, an adaptive scheme is proposed for the selection the best coding mode and best transform size. It is shown by experimental results that the super-macroblock coding scheme can achieve a higher coding gain. Keywords: high definition, super-macroblock, rate distortion optimization, integer transform

1. INTRODUCTION Since raw video data are voluminous in real world applications, video coding has studied for a wide range of applications in almost two decades with an objective to save transmission bandwidth and storage space. Many video coding standards have been established, including MPEG-1/2/4, H.261/H.263, and the latest H.264 standard by the Joint Video Team (JVT) [1]. As compared with earlier standards, H.264/AVC has achieved a significant improvement in the rate-distortion (RD) performance. That is, it is possible to save the bit rate up to 50% while keeping the video quality about the same. Today’s video coding standards are all based on a similar hybrid video coding framework, i.e. motion-prediction along the temporal domain and the DCT(discrete cosine transform)-based residual coding in the spatial domain. The significant improvement of H.264 mainly comes from the inclusion of many new coding modes and tools such as intra prediction, adaptive block transform [2], context adaptive variable length coding (CAVLC) [3], context adaptive binary arithmetic coding (CABAC) [4], variable block size motion compensation, multi-reference, quarter-pixel interpolation [5], loop-filtering [6], coding with direct and skip modes, etc. Besides these normative coding tools, some non-normative encoder optimization tools have also brought the performance improvement, such as the Lagrangian RD optimization [7], the Hadamard transform based motion search, the adaptive dead-zone quantization [8], etc. After the great success of H.264/AVC, JVT continues to work on several new topics, including scalable video coding (SVC) and multiview video coding. Scalable video coding is important for network-based video transmission and scalable quality service, and H.264/SVC is expected to play a role in real world applications. Standardization of the multiview video format is considered by JVT due to the promising future of stereo video display. Another longer term objective of JVT is to develop a new generation video coding standard which is significantly better than H.264/AVC, which could be named by H.265. In this research, we have attempted to identify the shortcomings of H.264 and seek ways to overcome them. Thus, our goal is in alignment with that of H.265. It is interesting to point out that most experimental results in the H.264 development were focused on low resolution video sequences of the QCIF and CIF formats. Some high definition (HD) video sequences were tested only under the high profile. However, the H.264 framework was almost fixed at that time. We argue that the macroblock-based partition in H.264 is not efficient when the spatial resolution of the input video goes higher (e.g., HD or super-HD video), since the maximum macroblock size, 16 × 16, is relatively small against the full image size. As HD video becomes more popular nowadays, it is worthwhile to develop a new video coding format that targets at effective coding of HD video contents. As a result, we will investigate a proper way to tailor the traditional prediction/transform framework to the HD video coding context in this work. Further author information: please send correspondence to Siwei Ma (E-mail:[email protected])

Actually, some special properties associated with HD video were already found in the H.264 development process. For example, in the quarter pixel interpolation process, the 6-tap filter shows better performance than the 4-tap filter for QCIF and CIF sequences. However, they give similar performance for HD sequences [9]. Recently, a larger block size was studied for super-HD coding in [10] by shifting the maroblock size 16 × 16 to 32 × 32 or even 64 × 64, and the idea has shown an outstanding coding gain at low bit rates. An integer transform of size 16 × 16 was studied in [11] for HD video coding, and a performance gain of more than 0.1dB was observed. The super-macroblock coding technique and the 16 × 16 integer transform are considered jointly to result in a more efficient HD coding scheme. There are several major contributions in our work. First, new super-macroblock coding modes are proposed and a new low-complexity 16 × 16 integer transform is proposed to go along with the super-macroblock prediction framework for HD video coding. Second, the RD charateristics of HD video is studied, and the RD characteristics of HD video is found to be different from that of low resolution video. When super-macroblock coding modes are introduced, the corresponding mode selection process should be modified. Here, we propose an adaptive Lagrangian coding control scheme for supermacroblock mode selection. It is shown by experimental results that the proposed super-macroblock coding scheme can achieve a higher coding gain than H.264. The rest of this paper is organized as follows. Review of previous work on super-macroblock prediction, 16 × 16 DCT transform and the Lagrangian coding control is conducted in Section 2. The super-macroblock coding scheme and the low complexity 16 × 16 integer transform are detailed in Section 3. The adaptive Lagrangian coding control algorithm is described in Section 4. Experimental results are shown in Section 5. Finally, some concluding remarks are given in Section 6.

2. REVIEW OF PREVIOUS WORK 2.1. Super-Macroblock Partition and Large-size Integer Transform In current video coding standards, the maximum block size is 16 × 16, which is called macroblock, and the transform block size is usually 8 × 8 and, sometimes, 4 × 4. These choices are made to seek a balance between computational efficiency and coding efficiency. Although the macroblock size is traditionally set to 16 × 16 in video coding standards, there have been studies on the use of blocks of a larger size in motion prediction and/or transform coding. For example, image coding with blocks of size 32 × 32 was considered in [12] under the context of quad-tree-based image coding and 32 × 32 block based video coding was investigated in [13]. For the larger size transform, the 16 × 16 transform was examined by researchers in the 80s, e.g. [14], [15]. The 16 × 16 DCT was approximated by an integer C-Matrix transform in [16]. The dyadic symmetric principle was used to develop the integer cosine transform in [17], where the principle was generalized to construct a larger 2N × 2N block size transform from a N × N transform using basis vector expansion and a 16 × 16 integer transform was used as an example. Most of these experiments were tested on lower resolution video before such as QCIF, CIF and SD video. With the increasing computational power of hardware, HD or even super HD video becomes popular now. Some new results of larger block size motion prediction and transform on HD or even super-high HD have been reported recently, e.g. the study by Naito et al. [10]. In Naito’s scheme, the maximum block size is set to 32 × 32 or 64 × 64 by scaling the block size of H.264 with a factor of 2 or 4. However, the DCT block size is still kept to be 4 × 4 or 8 × 8, which is the same as H.264. This scheme shows an outstanding coding gain at low bit-rates. In the development of H.264, the 16 × 16 integer transform was proposed by researchers such as [18], [19], [20], but not adopted due to the complexity and the ring artifact. The 16 × 16 integer transform for HD video coding was studied in [11], which is expanded from an 8 × 8 transform and called the compatible transform. It was shown that the 16 × 16 transform has a coding gain improvement for HD video, more than 0.1dB for all test sequences and up to 0.4dB for some test sequences.

2.2. Lagrangian Coding Control Proper selection of modes and transform sizes plays an important role on coding efficiency. Wiegand and Girod [7] proposed a method to determine the Lagrangian multiplier, which is used to find the optimal mode and transform size in a rate-distortion optimization framework. This is called the Lagrangian coding control, which has been adopted by H.264. This coding control process will be reviewed below. First, the problem mode and/or transform size selection can be expressed by a constraint optimization problem as min{D}: subject to R < Rc . It means that we would like to find a set of coding parameters to minimize a distortion function D under a given bit rate constrant bounded by Rc . This problem can be resolved using the Lagrangian multiplier method by selecting the coding parameters that minimizes the Lagrangian cost function J = D + λR. The minimum of the cost function is obtained by setting its derivative with respect to R to zero, i.e. dD + λ = 0. dR Thus, we have

dD . dR Given R and D values from the rate and distortion models, λ can be calculated. In [7], a typical high-rate approximation curve for entropy-constrained scalar quantization is used for the rate modeling, i.e. λ=−

R = a log2

b , D

and the distortion D is modeled as

(2Q)2 Q2 = , 12 3 in H.263, where Q is quantization parameter. As a result, D=

λ=−

dD = cQ2 , dR

where c=ln2/(3a), and an empirical value 0.85 is a good approximation of c. In [21], R and D are modeled with the ρ-domain model [22] as R = θ(1 − ρ), and D = σ2 e−α(1−ρ) . Thus, λ is derived as

λ = β(ln(σ2 /D + δ)D/R,

where β and δ are model parameters. As compared with that in [7], the scheme proposed in [21] is more adaptive to video characteristics.

3. SUPER-MACROBLOCK CODING AND LARGE-SIZE INTEGER TRANSFORM In this work, we consider a super-macroblock of size 32 × 32. All super-macroblock partition modes are shown in Fig. 1. Please note that our scheme is different from that in [10], where all modes are shifted from M × N to 2M ×2N. Furthermore, we do not use the Intra32 × 32 mode, but keep the Intra4 × 4 mode for intra coding since the Intra32 × 32 model is seldom selected as reported in [10], while Intra4 × 4 is efficient for images with textures. The adaptive block transform [2] provides an efficient way to improve coding efficiency. Since the block size of inter prediction has been scaled by 2 in the super-macroblock coding scheme as compared with that in H.264, we propose to add

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Figure 1. Super-macroblock coding modes.

one additional integer transform of size 16 × 16 when the prediction block is of size 16 × 16 or above. A low complexity 16 × 16 integer transform, which is obtained by expanding from the 8 × 8 transform in H.264, is derived. The following 8 × 8 integer transform is adopted by H.264:

T 8×8

  8 8    12 10   8 4   1  10 −3 =  8  8 −8    6 −12    4 −8  3 −6

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 8   −12   8   −10   8    −6    4    −3

T 16×16 is generated from T 8×8 with the following expansion rule:   T (i/2, j/2) j N N×N

The resultant T 16×16 is of the following form:   8 8 8 8   8 8 8 8   12 10 6 3   12 10 6 3   8 4 −4 −8   8 4 −4 −8   10 −3 −12 −6   1  10 −3 −12 −6 T 16×16 =  8  8 −8 −8 8   8 −8 −8 8   6 −12 3 10   6 −12 3 10   4 −8 8 −4   4 −8 8 −4   3 −6 10 −12   3 −6 10 −12

8 8 −3 −3 −8 −8 6 6 8 8 −10 −10 −4 −4 12 12

8 8 −6 −6 −4 −4 12 12 −8 −8 −3 −3 8 8 −10 −10

8 8 −10 −10 4 4 3 3 −8 −8 12 12 −8 −8 6 6

8 8 −12 −12 8 8 −10 −10 8 8 −6 −6 4 4 −3 −3

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                               

where i and j are integers with their range between 0 and 15. It can be shown that the resulting integer transform has a better approximation to the exact DCT and a higher coding gain as compared with that in [17]. The proof is omitted due to the space limitation. Instead, we examine the transform coding gain, which was used in [19] to evaluate the coding efficiency of different transforms. The coding gain of an orthonormal block transform is defined as [19] 1 PN−1 i=0 sii N G N = 10 · log10 Q 1/N , N−1 s i=0 ii where sii is the variance of the ith transformed coefficient. Fig. 2 compares the coding gain of the proposed integer transform and the transform obtained by [17]. The correlation coefficients ranges from 0.05 to 0.95. We see from the curves that the coding gain of our proposed transform is closer to that of the exact DCT. More experimental results will be given in Section 5. Another advantage of the poposed transform is that it can be decomposed and implemented with using existing 8 × 8 transform modules. As compared with 1D 8-point integer transform, the proposed 1D 16-point integer transform demands 16 more additions as shown in Fig. 3. The transform size selection is conducted based on the rate-distortion optimization as that for H.264. After the integer transform, adaptive quantization is adopted to improve coding efficiency. Finally, the context adaptive binary arithmetic coder (CABAC) using the updated context model is applied to encode the quantized transform coefficients.

4. ADAPTIVE LAGRIANGIAN CODING CONTROL The RD optimization framework is used for mode decision in H.264 as reviewed in Section 2. It is observed that the RD characteristics of HD video is different from that of low resolution video. Fig. 4 shows the RD curves of different macroblocks for SD and HD video. We see from the curves that macroblocks in the HD frame tend to be coded with fewer bits than that in the SD frame and different macroblocks have different RD curves. Chen and Garbacea [21] proposed a content adaptive model for lambda estimation based on the ρ-domain rate model [22] depending on the variance of the input source. Here, we propose a new content adaptive model using a generalized RD source model. The transformed coefficients are often modeled by the Laplacian distribution [23] p(x) =

∆ −∆|x| e , 2

Figure 2. The coding gain comparison between the exact DCT, the proposed integer transform and the integer transformed proposed in [17].

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Figure 3. Effective implementation of the proposed 1D 16-point integer transform.

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Figure 4. The RD characteristics of different macroblocks in SD and HD video.

where ∆ is the parameter and its variance is equal to √ σ=

2 . ∆

As derived in [24], we have the distortion model D=

Q ∆Q − ∆Q 2 − (1 + coth )e 2 , 2 ∆ 2 ∆

and the rate model R = −p0 (0) log2 p0 (0) − e− where S =

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log2 sinh

Q∆ ∆ + S, 2 log 2

Q∆ Q∆ X −iQ∆ ie and p0 (0) = 1 − e− 2 2 i=0

n

|xi | · p0 (xi ) = 2∆ · sin

i

for the Laplacian source. Then, we can express the Lagrange multiplier as λ=−

dD dD/dQ =− . dR dR/dQ

(1)

It is straightforward to show that dD dQ dR dQ where

(1 + γ2 )2 1 1 (1 + γ2 ) 1 + + )Q − − 2 2 3 3 2(γ − 1) γ 2γ (γ − γ) ∆(γ − γ) ∆γ X i2 ∆2 Q γ2 + 1 X i ∆ 2γ2 ∆(γ2 − 1) X i 2 ( − (γ − 1) ) + (log (γ + 1) − − 1) + 2 γ log 2 2 2γ γ log 2 γ2i γ2i (γ2 − 1) ln 2 γ2i

= ( =

∆Q

γ=e2 . We plot the theoretical Lagrange multiplier value, λ, as a function of QP parameterized by ∆ in Fig. 5. Note that the Lagrange multiplier increases with QP and decreases with ∆. The Lagrange multiplier value in H.264 is close to the theoretical value when ∆ = 0.01 as shown in Fig. 6. As derived above,√ we need parameter ∆ to calculate the Lagrange multiplier λ. The parameter ∆ can be computed from the variance as ∆ = σ2 , where σ can be approximated by [25] √ 2S AD . σ= 256

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Figure 5. The theoretical Lagrange multiplier value as a function of QP parameterized by ∆ for the Laplacian source.

Figure 6. Comparison between the theoretical Lagrange value with ∆=0.01 and the Lagrange values used in H.264.

Thus, the Laplacian parameter can be computed as √ 2 256 ∆= = . σ S AD Consequently, λ can be expressed as a function of S AD and QP; namely, λ = f (S AD, QP). In the macroblock mode decision process, we can get S ADi , ∆i and λi easily for each mode. On the other hand, the RD cost is computed as Ji = Di + λi Ri , where Di is the sum of squared differences (SSD). Please note that there is a discrepency between the distortion used for the λ calculation, which is S AD, and the distortion used in the RD cost, which is S S D. Thus, we use S S D (instead of S AD) to compute λ multiplied by a correction factor; namely, λ = k × f (S S D, QP), where k is the correction factor used to compensate the approximation of S AD by S S D.

5. EXPERIMENTAL RESULTS The suer-macroblock coding (SBM) scheme was implemented in the H.264 reference software JM9.8. First, the coding performance of the proposed integer transform was tested for several HD sequences. In the test, only 32 × 32, 32 × 16, 16 × 32, 16 × 16 modes were used with 16 × 16 transform. Fig. 7 compares the performance of the proposed integer transform and DCT on HD test sequences. We see that the proposed 16-point integer transform has very close performance as that of the exact DCT. Next, the super-macroblock coding scheme was compared with the JM reference code. The test conditions are given as Table 1. The performance comparison between H.264 (denoted by JVT) and the proposed super-macroblock coding

Figure 7. The coding performance comparison between the proposed 16-point integer transform (proposed T) and the exact DCT for the “Blue Sky” HD sequence (left) and the “Pedestrian Area” HD sequence (right).

scheme (denoted by SMB) is given in Fig. 8. As shown in the figure, the super-macroblcok scheme has much better performance than JM at the low bit rate with near 1.0dB for Sunflower and more than 0.5dB for Blue Sky. However, for sequences with complex motion such as Rush Hour, super-macroblock does not have a significant improvement. Table 1. Test conditions

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28, 32, 36, 40

In Fig. 9, we show the super-macroblock mode distribution at different QP values, where the skip mode and the direct mode refer to 32 × 32 blocks. The ratio of 16 × 16 blocks increases with decreasing QP. Also, there are more skipped blocks, fewer 16 × 16 modes in Blue Sky than that in Rush Hour, which explains why super-macroblock coding is more efficient for Blue Sky but less for Rush Hour. Finally, subjective comparison between sequences coded with the proposed super-macroblock/16×16-transform scheme and the original sequence is shown in Fig. 10. We see little distortion in both the texture and the flat areas. Thus, the proposed scheme offers a promising solution to future HD or super HD video coding.

6. CONCLUSION AND FUTURE WORK A super-macroblock-based scheme with 2D 16 × 16 block integer transform was investigated for HD video coding in this work. As compared with the conventional 1D 8-point integer transform, we only need 16 more additions in implementing the 1D 16-point integer transform. Adaptive lagrangian coding control was proposed for super-macroblock mode decision. It was shown by experimental results show that the proposed super-macroblock coding scheme can achieve a higher coding gain especially at low bit rates. Some preliminary results of the proposed super-macroblock-based scheme were given in this work. However, there are many problems worth further research and development. To extend the current work, we plan to do more work such as the super-macroblock mode design, efficient 16 × 16 block based entropy coding and other related issues (e.g., adptive quantization, adaptive scan) in the near future.

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(c) Figure 8. Coding efficiency comparison between H.264 and the proposed super-macroblock coding scheme.

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Figure 9. Mode distribution in super-macroblock coding.

Figure 10. Subjective visual comparison between a frame from the original sequence (top) and a reconstructed frame from the proposed scheme (bottom).

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