Video Super-Resolution Project
-Dynamic Texture Synthesis Method Selection

1Chih-Chun Hsu, 1Chia-Wen Lin, and Li-Wei Kang
1Department of Electrical Engineering
National Tsing Hua University
Hsinchu 30013, Taiwan

Description

to investigate the impact of DTS models on SR performance, we also implement two state-of-the-art nonlinear DTS models, High-order DTS (HO-DTS)  [23] and high-order-SVD-DTS (HOSVD-DTS) [34], to replace the linear model in (8) used in the proposed DTS-SR. Fig. 10 illustrates three reconstructed SR frames for Video #2 using linear DTS method [22], HO-DTS, and HOSVD-DTS. The complexities of the DTS methods in [23], [34] are significantly higher than that of the linear model in [22], whereas the visual qualities of the reconstructed HR videos using these three DTS .

Table 1 and Table 2 show the objective and subjective evaluation result comparison among three DTS methods in [22], [23], and [34]. Table 3 presents a fact that the computational complexity of the linear DTS in [22] is significantly lower than that of DTS methods in [23] and [34]. Furthermore, the MOVIE values of eight synthesized videos using the linear DTS in [22] is comparable with other two DTS methods in [23] and [34]. Table 3 also indicates that the subjectively visual quality of the reconstructed videos using these three DTS methods is very close to each other. In conclusion, we choose the linear DTS model in this paper. Besides, Fig. I.1 shows the visual quality of the synthesized result of video #2. It is clear that the difference of these three reconstructed videos using the DTS methods in [22], [23], and [34] is negligible.

 These additional experiments are presented in our website, which show that the synthesized dynamic textures using these three approaches are quite similar to each other, implying that the linear model for dynamic texture synthesis is good enough for this application.

 

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All test videos and super-resolved video using three different DTS methods in [22], [23], and [34] can be downloaded from here.

 

Visual Quality Comparison among Three DTS Methods

Video #1: (a) The ground truth frame and the reconstructed SR frames using (b) the linear model in [22], (c) HO-DTS model in [23], and (d) HOSVD-DTS in [34]. The visual quality of these methods are quite similar. In conclusion, we adopt the linear model in [22] to save the computational time.

Table 1: Subjective “visual quality” evaluation by paired comparisons (in relative winning percentage) for reconstructed HR video #1 obtained using the proposed method with various DTS models in [22], [23], and [34].

Method

Proposed + DTS in [22]

Proposed + DTS in [34]

Proposed + DTS in [23]

Average

Proposed + DTS in [22]

-

48%

53%

50.5%

Proposed + DTS in [34]

52%

-

53%

52.5%

Proposed + DTS in [23]

47%

47%

-

47.0%

Video #2: (a) The ground truth frame and the reconstructed SR frames using (b) the linear model in [22], (c) HO-DTS model in [23], and (d) HOSVD-DTS in [34]. The visual quality of these methods are quite similar. In conclusion, we adopt the linear model in [22] to save the computational time.

Table 2: Subjective “visual quality” evaluation by paired comparisons (in relative winning percentage) for the reconstructed HR video #2

Method

Proposed + DTS in [22]

Proposed + DTS in [34]

Proposed + DTS in [23]

Average

Proposed + DTS in [22]

-

49%

52%

50.5%

Proposed + DTS in [34]

51%

-

53%

52.0%

Proposed + DTS in [23]

48%

47%

-

47.5%

 

 

Video #3: (a) The ground truth frame and the reconstructed SR frames using (b) the linear model in [22], (c) HO-DTS model in [23], and (d) HOSVD-DTS in [34]. The visual quality of these methods are quite similar. In conclusion, we adopt the linear model in [22] to save the computational time.

Table 3: Subjective “visual quality” evaluation by paired comparisons (in relative winning percentage) for the reconstructed HR video #3

Method

Proposed + DTS in [22]

Proposed + DTS in [34]

Proposed + DTS in [23]

Average

Proposed + DTS in [22]

-

50%

50%

50.0%

Proposed + DTS in [34]

50%

-

51%

50.5%

Proposed + DTS in [23]

50%

49%

-

49.5%

 

 

Video #4: (a) The ground truth frame and the reconstructed SR frames using (b) the linear model in [22], (c) HO-DTS model in [23], and (d) HOSVD-DTS in [34]. The visual quality of these methods are quite similar. In conclusion, we adopt the linear model in [22] to save the computational time.

Table 4: Subjective “visual quality” evaluation by paired comparisons (in relative winning percentage) for the reconstructed HR video #4

Method

Proposed + DTS in [22]

Proposed + DTS in [34]

Proposed + DTS in [23]

Average

Proposed + DTS in [22]

-

45%

52%

48.5%

Proposed + DTS in [34]

55%

-

51%

53.0%

Proposed + DTS in [23]

48%

49%

-

48.5%

 

Video #5: (a) The ground truth frame and the reconstructed SR frames using (b) the linear model in [22], (c) HO-DTS model in [23], and (d) HOSVD-DTS in [34]. The visual quality of these methods are quite similar. In conclusion, we adopt the linear model in [22] to save the computational time.

Table 5: Subjective “visual quality” evaluation by paired comparisons (in relative winning percentage) for the reconstructed HR video #5

Method

Proposed + DTS in [22]

Proposed + DTS in [34]

Proposed + DTS in [23]

Average

Proposed + DTS in [22]

-

52%

51%

51.5%

Proposed + DTS in [34]

48%

-

52%

50.0%

Proposed + DTS in [23]

49%

48%

-

48.5%

 

 

Video #6: (a) The ground truth frame and the reconstructed SR frames using (b) the linear model in [22], (c) HO-DTS model in [23], and (d) HOSVD-DTS in [34]. The visual quality of these methods are quite similar. In conclusion, we adopt the linear model in [22] to save the computational time.

Table 6: Subjective “visual quality” evaluation by paired comparisons (in relative winning percentage) for the reconstructed HR video #6

Method

Proposed + DTS in [22]

Proposed + DTS in [34]

Proposed + DTS in [23]

Average

Proposed + DTS in [22]

-

53%

50%

51.5%

Proposed + DTS in [34]

47%

-

52%

49.5%

Proposed + DTS in [23]

50%

48%

-

49.0%

 

 

Video #7: (a) The ground truth frame and the reconstructed SR frames using (b) the linear model in [22], (c) HO-DTS model in [23], and (d) HOSVD-DTS in [34]. The visual quality of these methods are quite similar. In conclusion, we adopt the linear model in [22] to save the computational time.

Table 7: Subjective “visual quality” evaluation by paired comparisons (in relative winning percentage) for the reconstructed HR video #7

Method

Proposed + DTS in [22]

Proposed + DTS in [34]

Proposed + DTS in [23]

Average

Proposed + DTS in [22]

-

51%

49%

50.0%

Proposed + DTS in [34]

49%

-

51%

50.0%

Proposed + DTS in [23]

51%

49%

-

50.0%

 

Video #8: (a) The ground truth frame and the reconstructed SR frames using (b) the linear model in [22], (c) HO-DTS model in [23], and (d) HOSVD-DTS in [34]. The visual quality of these methods are quite similar. In conclusion, we adopt the linear model in [22] to save the computational time.

Table 7: Subjective “visual quality” evaluation by paired comparisons (in relative winning percentage) for the reconstructed HR video #7

Method

Proposed + DTS in [22]

Proposed + DTS in [34]

Proposed + DTS in [23]

Average

Proposed + DTS in [22]

-

51%

50%

50.5%

Proposed + DTS in [34]

49%

-

51%

50.0%

Proposed + DTS in [23]

50%

49%

-

49.5%

 

 

Objective & Subjective Quality Comparison

Table 1: Objective Evaluation by THE MOVIE Index for the Reconstructed SR Videos Obtained Using the Bicubic [3], NLIBP-SR, [6] ASDS-SR [11], TS-SR [26],, the proposed method, the proposed method with HOSVD-DTS [34], and the proposed method with DTS in [23] (Smaller MOVIE Value Indicates Higher Visual Quality).

 

Table 2: Objective “visual quality” evaluation by paired comparisons (in relative winning percentage) for the eight reconstructed HR videos obtained using the proposed method with the linear DTS methods in [22], .[23], and [34].

 

Table 3: Rum-time (in seconds) comparisons among the different DTS methods in [22], [23], and [34] (We only take texture synthesis processing into account).

 

 

 

References

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[9] J. Yang, J. Wright, T. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process., vol. 19, no. 11, pp. 2861–2873, Nov. 2010.
 [11] W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process., vol. 20, no. 7, pp. 1838−1857, July 2011.
[22] G. Doretto, A. Chiuso, Y. N. Wu, and S. Soatto, “Dynamic textures,” Int. J. Comput. Vis., vol. 51, no. 2, pp. 91−109, 2003.
[23] M. Hyndman, A. D. Jepson, and D. J. Fleet, “Higher-order autoregressive models for dynamic textures,” in Proc. British Machine Vis. Conf., Warwick, Sept. 2007, pp. 1−10.
[26] Y. HaCohen, R. Fattal, and D. Lischinski, “Image upsampling via texture hallucination,” in Proc. IEEE Int. Conf. Comput. Photography, Cambridge, MA, USA, pp. 20−30, Mar. 2010.
[32] J.-S. Lee, “On designing paired comparison experiments for subjective multimedia quality assessment,” IEEE Trans. Multimedia, vol. 16, no. 2, pp. 564–571, Feb. 2014.
[34] G. Zhao, and P. Matti, “Local binary pattern descriptors for dynamic texture recognition,” in Proc. of 18th EEEE International Conference on Pattern Recognition (ICPR). Vol. 2. 2006.