Flat-region Detection And False Contour Removal In The Digital TV Display

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1 Flat-region Detection And False Contour Removal In The Digital TV Display IEEE International ICME 2005 Wonseok Ahn, and Jae-Seung Kim Presented by Shu Ran School of Electrical Engineering and Computer Science Kyungpook National Univ.

2 Abstract False contour by bit-depth reduction Coming from various display limitation Video memory constraints Physical characteristics of display Display drivers Coarse MPEG quantization Proposed method False contour detecting and segmenting flat-region Removing false contour by bit-depth extension 2/15

3 Introduction Bit-depth reduction in digital display Including digital TVs Current LCD displays displaying 8 bit-gray level at most For each RGB signal False contour appearing in region Pixel values smoothly varying originally» Low frequency and smooth gradient Defined as flat-region Irritating to human eye False contour more visible by video enhancement processing Histogram equalization Contrast enhancement Increasing sharpness 3/15

4 Previous method for removing false contour Using dithering with gamma correction Using dithering as pre-processing and LPF as post-processing Trade-off between removing false contour and blurring object edges Feedback-based quantization Detecting and minimizing false contour 4/15

5 Proposed method Flat-region detection & segmentation For applying bit-depth extension process Leaving object edges unprocessed Preventing blurring entire image Using both entropy and second-order statistics of local region Centering at current pixel Calculating local entropy i, j P log P y 2 y (1) y pixel valuesinblock i, j where P is the probability of the pixel value y in the current block y 5/15

6 Local deviation» Representing local contrast C i, j m, nblocki, j, I i, j 2 I m n (2) M N where i, j is the coordinate of the current pixel, mn, is the coordinate of neighboring pixels, I is the pixel value at mn,, I is the mean of pixel values in the current block Generating flat-region map MF i, j 1 where i, j C i, j THR 1 and i, j THR 2 0 elsewhere where THR1, THR2 are predefined threshold values for the local deviation and the local entropy respectfully (3) 6/15

7 Process of flat-region detection & segmentation Fig. 1. Flat-region detection & segmentation. 7/15

8 Bit-depth extension for removing false contour Two most common solutions to increase bit-depth Dithering technique Low-pass filter Not an effective solution for removing false contour» As pre-processing Proposed method Composed of three consecutive processing block Random Shuffler LPF» Making smooth transition between processed region and unprocessed Dithering 8/15

9 Random Shuffler Shuffling pixel locations randomly in a local block,,,, _, swap I i j I rand i rand j and rand i rand j Block rand i j (4) where rand is a random number index that is generated from the uniform distribution, and Block _ rand i, j is the local block for the current pixel at i, j Removing false contour by sprinkling them over neighboring regions» Making noise-like pattern» Applying LPF to image Displaying output of LPF on digital displays Re-quantized LPF image into display s own bit-resolution» Using error diffusion 9/15

10 Process of bit-depth extension Fig. 2. Bit-depth extension in the flat-region map. Generally L-bit Q-bit K-bit in most of the current display. 10/15

11 Data for experiment Results Computer graphic pattern and real video images Containing false contours Human eyes easily to discernable Original graphic pattern (a) (b) (c) (d) Fig. 3. (a) A graphic pattern with smooth gradient, (b) Detected flatregion, (c) Mapping function, and (d) Output of mapping function with false contour appearing 11/15

12 Low pass filtered image (a) Fig. 4. (a) Output of LPF only for figure 3(a), and (b) Output of mapping function shown in figure 3(c) Result of proposed method (b) (a) (b) Fig. 5. (a) Output of the proposed method, and (b) Output of mapping function shown in figure 3(c) 12/15

13 Frame of video sequence (a) (b) (c) (d) Fig. 6. (a) A video image of 6-bit depth, (b) Detected flat-region map of (a), (c) Result of only the LPF applied, and (d) Result of the proposed method ; (c) and (d) show the magnified area of the boxed region in (a) 13/15

14 Profile comparison of luminance signal Fig. 7. The horizontal profile of the luminance signal for the thick line drown over the image in figure 6. 14/15

15 Proposed method Flat-region detection False contours occur most Leaving object edges Bit-depth extension Random Shuffler LPF Dithering Conclusion 15/15

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