Difference between revisions of "BlockGraphTransforms"
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<P><B>Edge adaptive transform (EAT) design</B></P> | <P><B>Edge adaptive transform (EAT) design</B></P> | ||
<p> | <p> | ||
| − | + | The EAT design | |
| − | process consists of three steps, i.e., (i) edge detection is applied on the residual block to | + | process consists of three steps, i.e., (i) edge detection is applied on the residual block to find edge locations, (ii) a graph |
is constructed based on the edge map, then (iii) an EAT is | is constructed based on the edge map, then (iii) an EAT is | ||
| − | constructed and EAT | + | 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 coef?cients is | ||
applied. The 2 × 2 sample block in Fig. 1 is used to illustrate | applied. The 2 × 2 sample block in Fig. 1 is used to illustrate | ||
Revision as of 10:17, 7 September 2012
Edge-adaptive transforms for efficient depth map coding
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 creating large high frequency coef?cients. 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.
Edge adaptive transform (EAT) design
The EAT design process consists of three steps, i.e., (i) edge detection is applied on the residual block to find edge locations, (ii) a graph 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 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.
