JPEG encoding is a common technique to compress images. However, since JPEG is a lossy compression certain artifacts may occur in the compressed image. These artifacts typically occur in high frequency or detailed areas of the image. This paper proposes an algorithm based on the SSIM metric to improve the experienced quality in JPEG encoded images. The algorithm improves the quality in detailed areas by up to 1.29 dB while reducing the quality in less detailed areas of the image, thereby increasing the overall experienced quality without increasing the image data size. Further, the algorithm can also be used to decrease the file size (by up to 43%) while preserving the experienced image quality. Finally, an efficient GPU implementation is presented. © 2014 IEEE.

This paper introduces a new metric for approximating structural instability in Bayer image data. We show that the metric can be used to identify and classify validity of color correlation in local image regions. The metric is used to improve interpolation performance of an existing state of-the-art single pass linear demosaicing algorithm, with virtually no impact on computational GPGPU complexity and performance. Using four different image sets, the modification is shown to outperform the original method in terms of visual quality, by having an average increase in PSNR of 0.7 dB in the red, 1.5 dB in the green and 0.6 dB in the blue channel respectively. Because of fewer high-frequency artifacts, the average output data size also decreases by 2.5%. © 2016 IEEE.