  Codecs using spectral-spatial transforms efficiently compress raw camera
images captured with a color filter array (CFA-sampled raw images) by changing
their RGB color space into a decorrelated color space. This study describes two
types of spectral-spatial transform, called extended Star-Tetrix transforms
(XSTTs), and their edge-aware versions, called edge-aware XSTTs (EXSTTs), with
no extra bits (side information) and little extra complexity. They are obtained
by (i) extending the Star-Tetrix transform (STT), which is one of the latest
spectral-spatial transforms, to a new version of our previously proposed
wavelet-based spectral-spatial transform and a simpler version, (ii)
considering that each 2-D predict step of the wavelet transform is a
combination of two 1-D diagonal or horizontal-vertical transforms, and (iii)
weighting the transforms along the edge directions in the images. Compared with
XSTTs, the EXSTTs can decorrelate CFA-sampled raw images well: they reduce the
difference in energy between the two green components by about $3.38$--$30.08$
\% for high-quality camera images and $8.97$--$14.47$ \% for mobile phone
images. The experiments on JPEG 2000-based lossless and lossy compression of
CFA-sampled raw images show better performance than conventional methods. For
high-quality camera images, the XSTTs/EXSTTs produce results equal to or better
than the conventional methods: especially for images with many edges, the
type-I EXSTT improves them by about $0.03$--$0.19$ bpp in average lossless
bitrate and the XSTTs improve them by about $0.16$--$0.96$ dB in average Bj\o
ntegaard delta peak signal-to-noise ratio. For mobile phone images, our
previous work perform the best, whereas the XSTTs/EXSTTs show similar trends to
the case of high-quality camera images.
