I have been working with industrial cameras and frame-grabbers that simply gives me a dump of their sensor data. To make that data useful it needs to undergo demosaicing, ideally using one of the modern demosaic algorithms like RCD rather than simple interpolation or the rather outdated VNG. Unfortunately the latter are all that OpenCV supports.
To gain access to more advanced demosaic algorithms and sophisticated image processing functions like colour calibration, micro-contrast, dehaze, etc, I encoded the raw colour filter array (CFA) data into DNG using the PiDNG library. With compression disabled this library produces DNGs which is accepted by darktable. However if I enable compression darktable is unable to open it.
Running darktable from the command line, the following error is observed:
71.5724 [rawspeed] (compression-test.dng) void rawspeed::AbstractDngDecompressor::decompress() const, \
line 250: Too many errors encountered. Giving up. First Error: virtual Buffer::size_type \
rawspeed::LJpegDecoder::decodeScan(), line 108: Unsupported predictor mode: 6
This suggests that the predictor used is not supported. By editing the source code of PiDNG, predictor was set to 1 which results in yet another error:
10.7300 [rawspeed] (compression-test.dng) void rawspeed::AbstractDngDecompressor::decompress() const, \
line 250: Too many errors encountered. Giving up. First Error: virtual Buffer::size_type \
rawspeed::LJpegDecoder::decodeScan(), line 141: Maximal output tile size is not a multiple of LJpeg frame size
This error happens because PiDNG uses an optimisation trick to improve the compression ratio. Consider a typical CFA pattern:
RG GB
The raw sensor data is essentially the above repeated horizontally and vertically:
RGRGRG GBGBGB RGRGRG GBGBGB RGRGRG GBGBGB
Lossless JPEG compressed by using a predictor that uses previous pixel values, including those in the previous row. Such a predictor work better if correlated values are closer together. So instead of saving the image as-is, the DNG specification (as of 1.4.0) allows the image data to be stored as an image that is twice as wide but halve as high (preserving the total number of pixels):
RGRGRGGBGBGB RGRGRGGBGBGB RGRGRGGBGBGB
In this new image, correlated pixels are closer together. e.g. the first red pixel had no immediate neighbours that was also red, but now it has an immediate neighbour in the next row. The rawspeed library however doesn't support this optimisation trick (or predictors other than 1), which to be fair is also useless with predictor 1, which only looks at the previous value on the same row. This trick is most useful with predictors that look at values from previous rows, e.g. mode 6 which is the default mode used by PiDNG.
With another modification to the PiDNG library that encodes the sensor data as a JPEG image of the original size, we were able to produce a losslessly compressed DNG file that darktable opens. The compression ratio isn't as good - 80% vs 75%. Hopefully darktable will gain support for predictors 2-7 and we can get better compression. In the meantime I cleaned up the changes to PiDNG and have submitted a PR which will hopefully be accepted.
Rant
DNG v1.4.0 was released in 2012, a time when DEFLATE compression in TIFF files was widely used yet for some reason the DNG specification restricts its use to floating point image data with estoic lossless JPEG, defined 20 years ago in 1992, being the only other lossless compression option. It is not until 2023, with DNG v1.7.0, that JPEG-XL is added for integer image data. Unfortunately JPEG-XL is exotic enough that darktable still doesn't support it in 2025. Why we couldn't have allowed Deflate for integer image data is beyond me. So much space could have been be saved.