Constrained MPWIP

With respect to the observation that the weighting functions of the DC and AC components of the same layer have similar waveforms (although their magnitudes can differ considerably), in this section we propose a Constrained MPWIP, in which the weight values to associate with the DC and AC components from the same layer are restricted to be identical or, alternatively, the texture information of the EL intra predictor and the reconstructed BL block are to be weighted. There is an additional constraint; that the weight values for these aforementioned texture information must

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Table 12: Performance of the Constrained-MPWIP with respect to SHM 1.0

AI HEVC 2x AI HEVC 1.5x

Figure 17: The Constrained MPWIP Scheme

add up to one; the constraint is referred to as the unit-gain constraint [9]. The use of this constraint is due to the fact that each of these textures can be considered as a prediction candidate; therefore, for the worst case, the sum of these textures could be blown out to the range of values that predefined the sample. Essentially, this Constrained MPWIP becomes one that forms a prediction of the EL by linearly

combining the reconstructed BL block and the EL (directional) intra predictor with an adaptive weighting at the pixel level, as illustrated in Figure 17, and that is therefore similar to the scheme proposed in [6]. Furthermore, it is observed that all the unifying

EL Intra

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Figure 18: Waveform of weighting functions for all test modes at block size of 16x16, QP(30,30). (a) Planar mode, (b) DC mode, (c) Horizontal mode, (d) Vertical mode

techniques found in the previous section can be applied to this scheme. Therefore, when viewed as a simplified version of MPWIP, this scheme can be considered as an extension of the MPWIP-U scheme and requires only 21 weight tables, one (rather than four as in the proposed design) weighting function for each EL prediction mode, only a single set of weight tables for all QP settings, unifying weight tables for the Horizontal and Vertical modes and for different block sizes which are the luma/chroma type. As an example, Figure 18 depicts the weighting function for the EL intra predictor produced with four test modes for the luminance component at the block size of 16x16.

Table 12 shows the coding performance of the constrained MPWIP, as compared to the SHM-1.0 anchor. As expected, it incurs a moderate coding loss of 0.5% and

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0.3% in the AI-2x and AI-1.5x cases, respectively, when compared to the original design. This provides beneficial separation of signals into DC and AC components in forming a better EL intra predictor.

In an attempt to examine the effect of the unit gain constraint in terms of bit rate savings, the supplementary experiments were carried out with all the test conditions being the same as those of the Constrained MPWIP, except that the ‘unit gain constraint’ was relaxed. It can be concluded that the bit rate savings of this

supplementary experiment varies inconsiderably compared with the ‘Constrained MPWIP’. In addition, the supplementary experiment has a higher cost in terms of increasing the number of weight tables to twice as many.

Conceptually, the Constrained MPWIP scheme is very similar to the proposed WIP algorithm in which the texture information of the BL and the EL are combined adaptively according to the pixel’s position. Therefore, it is beneficial to make a comparison between the Constrained MPWIP and the WIP algorithm. From Table 6 and Table 12, it can be seen that our simplified mode provides a 0.5% coding gain for the AI-2x and 0.3% for AI-1.5x cases respectively, while the WIP algorithm achieved 0.3% and 0.1% gains respectively. There are two major differences between our scheme and the WIP algorithm that enables our algorithm to provide a higher gain: 1) we created four additional modes and the best mode for each block is determined by the RDO process, while the WIP algorithm is applied to all intra prediction modes; 2) it seems that the WIP algorithm uses a unified weighting scheme for all the intra prediction modes, even though there are cases where it is not necessary to weight some neighboring reference pixels in the EL; in our mode, we have different weighting

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functions for each intra mode, which leads to a more appropriate weighting scheme to form the final prediction. However, each mode in our scheme has its own weighting functions and the number of weight tables also depends on the prediction block size, so that the cost of our scheme is higher in terms of memory storage to store those weight tables.

4.3.3. Summary

The findings of this chapter would be summarized as follows

 It turns out that the performance of the proposed algorithm (MPWIP)

depends strongly on the characteristics of the video sequences. Specifically, the proposed scheme works best in sequences that contain more homogeneous regions.

 For the Unification to MPWIP simplifications, it can be concluded that the

number of weight tables is reduced considerably with moderate R-D losses, compared to MPWIP.

 For the Constrained MPWIP, even though the number of weight tables can be

further reduced, the losses seem to be significant, compared to MPWIP.

Therefore, it justifies the benefits of separating the texture information into the AC and DC components.

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CHAPTER 5

Conclusions

5.1. Summary

In this thesis, we have introduced a sophisticated algorithm to combine the EL intra predictor and the BL reconstructed block targeted to improve the EL intra prediction in the framework of the TextureRL; and this algorithm does bring a coding gain. The algorithm first separates those textures into the AC and DC components, and then weights them by different weight values; the weight value to associate with each component is a function of the prediction pixel’s position in the block. In addition, the parameters (e.g. the intra prediction mode of the EL intra predictor, the QP setting, and the prediction block size) that affect the weighting scheme were thoroughly analyzed.

From those analyses, the important observations can be summarized as follows:

1. The weighting functions associated with each AC and DC component of both layers are dependent on the prediction mode of the EL intra predictor,

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2. The effect of the QP setting on the weighting functions is insignificant in terms of bit rate savings in the common test conditions, and

3. The weighting functions depend quite significantly on the prediction block size in which the higher weight values are given to the weighting functions associated with the EL components in the smaller block size.

However, the simulation results reveals that the coding gain from the proposed design is rather limited, and may not justify the significant increase in computational complexity and memory requirement to store the weight tables. In addition, though its simplified versions (e.g. Unification to MPWIP simplifications, and Constrained-MPWIP) can reduce the memory requirement, the coding gains are also reduced considerably. Furthermore, a similar observation was also made for other tools adopting the TextureRL approach [11]. Therefore, it may not be worthwhile to construct the scalable extension to HEVC in the TextureRL framework.

5.2. Future Works

Instead of applying the proposed weighting scheme to the AC and DC components, more sophisticated components derived from the texture information can be considered. Those components can be extracted from the texture information by different types of filtering methods. Therefore, a more thorough analysis on the frequency components of the texture information can be taken into account as an extension of this thesis.

The other aspect that we have not fully explored in this thesis is whether the costly floating-point operation that occurs in our weighting scheme is necessary. It can

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perhaps be replaced by a simpler fixed-point operation; however, the impact on coding performance remains to be investigated.

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