Bayer Pattern Color Filter Arrays (CFAs) are widely used in digital photo and video cameras. Generally these images are
corrupted by a signal and exposure dependent quantum noise. An automatic image processing carrying out within
camera usually implies a gamma and color corrections and an interpolation. And at the same time the noise in the image
becomes non-quantum and spatially correlated. This results in a drastic decrease of posterior noise reduction.
Considerably better quality of output images can be provided if non-processed Bayer Pattern CFAs (in RAW format) are
extracted from a camera and processed on PC. For this case, more effective noise reduction can be achieved as well as
better quality image reconstruction algorithms can accessed. The only drawback of storing images in a camera in RAW
format is their rather large size. Existing lossless image compression methods provide image compression ratios (CRs)
for such images of only about 1.5...2 times. At the same time, a posterior filtering in addition to noise reduction results
in appearing losses in the image. Therefore, the use of lossy image compression methods is defensible in this case while
final decreasing of effectiveness of noise reduction is inessential. The paper describes a method of adaptive selection of
quantization step for each block of a Bayer Pattern CFAs for DCT based image compression. This method allows
restricting the decreasing of the posterior noise reduction by only 0.25...0.3 dB. Achieved CRs for the proposed scheme
are by 2.5...5 times higher than for strictly lossless image compression methods.
A practical impossibility of prediction of signs of DCT coefficients is generally accepted. Therefore each coded sign of
DCT coefficients occupies usually 1 bit of memory in compressed data. At the same time data of all coded signs of DCT
coefficients occupy about 20-25% of a compressed image. In this work we propose an effective approach to predict signs
of DCT coefficients in block based image compression. For that, values of pixels of already coded/decoded neighbor
blocks of the image are used. The approach consist two stages. At first, values of pixels of a row and a column which
both are the nearest to already coded neighbor blocks are predicted by a context-based adaptive predictor. At second
stage, these row and column are used for prediction of the signs of the DCT coefficients. Depending on complexity of an
image proposed method allows to compress signs of DCT coefficients to 60-85% from their original size. It corresponds
to increase of compression ratio of the entire image by 3-9% (or up to 0.5 dB improvement in PSNR).
In this contribution, we study the problem of lossless compression of Bayer pattern CFA data. Two main issues are addressed: how to pack (reorder) pixels from the red, green and blue color planes into a structure appropriate for the subsequent compression algorithm (structural transformation) and how to utilize possible correlations between colors.
While structural transformation to the red and blue color planes is straightforward as they are directly downsampled onto compact rectangular grids, the quincunx sampling grid of the green pixels allows different separations. We explore three different methods for green pixels separation and compare their peculiarities in the light of the chosen prediction-based compression algorithm, i.e. JPEG-LS.
Two color decorrelation approaches are proposed as well. In the first approach, simple difference between the red or blue pixel and the corresponding nearest green pixel is calculated. In the second approach, several nearest green pixels are used to estimate the real green pixel value for the particular red or blue pixel location.
The performance of the proposed algorithm is tested for real CFA raw data and results in terms of compression ratio are presented.
In this contribution, we propose a near lossless compression algorithm for Color Filter Arrays (CFA) images. It allows higher compression ratio than any strictly lossless algorithm for the price of some small and controllable error.
In our approach a structural transformation is applied first in order to pack the pixels of the same color in a structure appropriate for the subsequent compression algorithm. The transformed data is compressed with a modified version of the JPEG-LS algorithm. A nonlinear and adaptive error quantization function is embedded in the JPEG-LS algorithm after the fixed and context adaptive predictors. It is step-like and adapts to the base signal level in such a manner that higher error values are allowed for lighter parts with no visual quality loss. These higher error values are then suppressed by gamma correction applied during the image reconstruction stage. The algorithm can be adjusted for arbitrary pixel resolution, gamma value and tolerated error range.
The compression performance of the proposed algorithm has been tested for real CFA raw data. The results are presented in terms of compression ratio versus reconstruction error and the visual quality of the reconstructed images is demonstrated as well.