BlockGraphTransforms - Revision history

Wikiboss at 18:07, 5 February 2016

2016-02-05T18:07:18Z

Sunil at 19:06, 7 September 2012

2012-09-07T19:06:15Z

Sunil at 19:04, 7 September 2012

2012-09-07T19:04:39Z

Sunil at 19:00, 7 September 2012

2012-09-07T19:00:49Z

Sunil at 18:59, 7 September 2012

2012-09-07T18:59:44Z

Sunil at 18:55, 7 September 2012

2012-09-07T18:55:00Z

Sunil at 18:50, 7 September 2012

2012-09-07T18:50:17Z

Sunil at 18:43, 7 September 2012

2012-09-07T18:43:57Z

Sunil at 18:41, 7 September 2012

2012-09-07T18:41:16Z

Sunil at 18:32, 7 September 2012

2012-09-07T18:32:23Z

@@ Line 84: / Line 84: @@
 <li>''' Software '''
 <ul>
-<li> [http://biron.usc.edu/~kumarsun/Codes/GraphTransform_Matlab.zip EAT transform demo]
+<li> [http://sipi.usc.edu/~ortega/Software/GraphTransform_Matlab.zip EAT transform demo]
 </ul>
 <br>

@@ Line 3: / Line 3: @@
 In this work a new set of edge-adaptive transforms (EATs) is
 presented as an alternative to the standard DCTs used in image and video coding applications. These transforms avoid
-?ltering across edges in each image block, thus, they avoid
+filtering across edges in each image block, thus, they avoid
-creating large high frequency coef?cients. These transforms
+creating large high frequency coefficients. These transforms
 are then combined with the DCT in H.264/AVC and a transform mode selection algorithm is used to choose between
 DCT and EAT in an RD-optimized manner. These transforms
 are applied to coding depth maps used for view synthesis in a
 multi-view video coding system, and provides up to 29% bit
-rate reduction for a ?xed quality in the synthesized views.
+rate reduction for a fixed quality in the synthesized views.
 </p>
 <P><B>Edge adaptive transform (EAT) design</B></P>
@@ Line 26: / Line 26: @@
 <li>'''EAT construction'''
 <ul>
-First edge detection is applied on the residual block to ?nd
+First edge detection is applied on the residual block to find
 edge locations and a binary edge map is generated that indicates the locations of edges. If no edges are found in a residual block, then a DCT is used and no further EAT processing
 is performed. Otherwise, the encoder computes an EAT. A
@@ Line 34: / Line 34: @@
 them. From this graph, we derive the [http://en.wikipedia.org/wiki/Adjacency_matrix adjacency matrix], [http://en.wikipedia.org/wiki/Degree_matrix diagonal degree matrix], and the [http://en.wikipedia.org/wiki/Laplacian_matrix Laplacian matrix]. Projecting the signal of a graph
 G onto the eigenvectors of the Laplacian matrix yields a spectral decomposition of the signal, i.e., it provides a “frequency
-domain” interpretation of the signal on the graph. EAT coef?cients are computed as the projection of the one-dimensionalized image-signal onto the eigenvectors
+domain” interpretation of the signal on the graph. EAT coefficients are computed as the projection of the one-dimensionalized image-signal onto the eigenvectors
-of the Laplacian matrix. The coefficients are quantized with a uniform scalar quantizer. This vector of coef?cients is then reformed
+of the Laplacian matrix. The coefficients are quantized with a uniform scalar quantizer. This vector of coefficients is then reformed
 into an N × N block by placing the coefficients in the
 standard zig-zag fashion used for the DCT.
@@ Line 42: / Line 42: @@
 <li> '''RD optimization'''
 <ul>
-The EATs minimize the number of non-zero coef?cients
+The EATs minimize the number of non-zero coefficients
 that must be encoded for a piece-wise constant(PWC) image, thus, they provide
-a highly ef?cient representation for depth maps (since depth
+a highly efficient representation for depth maps (since depth
 maps are nearly PWC). Since residual depth maps (resulting
-from intra or inter prediction) are also PWC, EATs also provide an ef?cient representation for
+from intra or inter prediction) are also PWC, EATs also provide an efficient representation for
 residual depth maps. However, since edge information must be encoded and sent to the
 decoder, these EATs are not necessarily RD-optimal, i.e., if

@@ Line 76: / Line 76: @@
 <ul>
     <li> [http://sipi.usc.edu/~ortega/ Antonio Ortega]
     <li> [http://biron.usc.edu/wiki/index.php/Sunil_Kumar Sunil K Narang]
 </ul>
@@ Line 82: / Line 84: @@
 <li>''' Software '''
 <ul>
-<li> [http://biron.usc.edu/wiki/index.php/Graph_Filterbanks Graph-QMF Filterbanks]
+<li> [http://biron.usc.edu/~kumarsun/Codes/GraphTransform_Matlab.zip EAT transform demo]
 </ul>
 <br>

← Older revision		Revision as of 19:00, 7 September 2012
Line 72:		Line 72:
	<li>		<li>
	G. Cheung, W.-S. Kim, A. Ortega, J. Ishida, and A. Kubota, "Depth map coding using graph based transform and transform domain sparsification," Proc. of IEEE Intl. Workshop on Multimedia Signal Proc., Hangzhou, China, Oct. 2011. (Top 10% Paper Award)</ul>		G. Cheung, W.-S. Kim, A. Ortega, J. Ishida, and A. Kubota, "Depth map coding using graph based transform and transform domain sparsification," Proc. of IEEE Intl. Workshop on Multimedia Signal Proc., Hangzhou, China, Oct. 2011. (Top 10% Paper Award)</ul>
		+	<br><br>
		+	<li>'''People'''
		+	<ul>
		+	<li> [http://sipi.usc.edu/~ortega/ Antonio Ortega]
		+	<li> [http://biron.usc.edu/wiki/index.php/Sunil_Kumar Sunil K Narang]
		+	</ul>
		+	<br>
		+
		+	<li>''' Software '''
		+	<ul>
		+	<li> [http://biron.usc.edu/wiki/index.php/Graph_Filterbanks Graph-QMF Filterbanks]
		+	</ul>
		+	<br>

← Older revision		Revision as of 18:59, 7 September 2012
Line 60:		Line 60:
	</ul>		</ul>
	[[http://biron.usc.edu/~kumarsun/PCS10_fig2.pdf] Figure 2]		[[http://biron.usc.edu/~kumarsun/PCS10_fig2.pdf] Figure 2]
		+
		+
		+	<li>'''Publications'''
		+	<ul>
		+	<li>
		+	G. Shen, W.-S. Kim, S. K. Narang, A. Ortega, J. Lee, and H. Wey, "Edge-adaptive transforms for efficient depth-map coding," in Proc. of 28th Picture Coding Symposium (PCS ‘10), Nagoya, Japan, Dec. 2010. [http://biron.usc.edu/~wooshikk/PCS10.pdf <small><b>PDF format</b></small>]</ul>
		+	<ul>
		+	<li>
		+	W.-S. Kim, S. K. Narang, and A. Ortega, "Graph based transforms for depth video coding," Proc. of IEEE Intl. Conf. Acoustics, Speech, and Signal Proc. (ICASSP 2012), Kyoto, Japan, Mar. 2012.</ul>
		+	<ul>
		+	<li>
		+	G. Cheung, W.-S. Kim, A. Ortega, J. Ishida, and A. Kubota, "Depth map coding using graph based transform and transform domain sparsification," Proc. of IEEE Intl. Workshop on Multimedia Signal Proc., Hangzhou, China, Oct. 2011. (Top 10% Paper Award)</ul>

@@ Line 22: / Line 22: @@
 can be easily applied to original pixel values.
 </p>
-[[http://biron.usc.edu/~kumarsun/PCS10_fig1.pdf]Figure 1]
+[[http://biron.usc.edu/~kumarsun/PCS10_fig1.pdf] Figure 1]
 <li>'''EAT construction'''
@@ Line 59: / Line 59: @@
 context-adaptive binary arithmetic coding [http://en.wikipedia.org/wiki/Context-adaptive_binary_arithmetic_coding (CABAC)].
 </ul>

@@ Line 32: / Line 32: @@
 residual block is connected to each of its immediate neighbors
 (e.g., 4-connected neighbors) only if there is no edge between
-them. From this graph, we derive the [http://en.wikipedia.org/wiki/Adjacency_matrix adjacency matrix], [http://en.wikipedia.org/wiki/Degree_matrix diagonal degree matrix], and the [http://en.wikipedia.org/wiki/Laplacian_matrix Laplacian matrix].
+them. From this graph, we derive the [http://en.wikipedia.org/wiki/Adjacency_matrix adjacency matrix], [http://en.wikipedia.org/wiki/Degree_matrix diagonal degree matrix], and the [http://en.wikipedia.org/wiki/Laplacian_matrix Laplacian matrix]. Projecting the signal of a graph
 </ul>
 <br>

@@ Line 17: / Line 17: @@
 is constructed based on the edge map, then (iii) an EAT is
 constructed and EAT coefficients are computed. The EAT coefficients are then quantized using a uniform scalar quantizer
-and the same run-length coding used for DCT coef?cients is
+and the same run-length coding used for DCT coefficients is
 applied. The 2 × 2 sample block in Fig. 1 is used to illustrate
 the main ideas. We describe the encoder operation when applied to blocks of prediction residuals, though the same ideas
 can be easily applied to original pixel values.
 </p>