Difference between revisions of "BlockGraphTransforms"
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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 coefficients are computed. The EAT coefficients are then quantized using a uniform scalar quantizer | 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 | + | and the same run-length coding used for DCT coefficients 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 | ||
the main ideas. We describe the encoder operation when applied to blocks of prediction residuals, though the same ideas | 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. | can be easily applied to original pixel values. | ||
</p> | </p> | ||
| + | [[http://biron.usc.edu/~kumarsun/PCS10_fig1.pdf]] | ||
Revision as of 10:32, 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 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.
[[1]]
