Improvements to JPEG-LS via diagonal edge-based prediction

JPEG-LS is the latest pixel based lossless to near lossless still image coding standard introduced by the Joint Photographic Experts Group (JPEG) '. In this standard simple localized edge detection techniques are used in order to determine the predictive value of each pixel. These edge detection techniques only detect horizontal and vertical edges and the corresponding predictors have only been optimized for the accurate prediction of pixels in the locality of horizontal and/or vertical edges. As a result JPEG-LS produces large prediction enors in the locality of diagonal edges. In this paper we propose a low complexity, low cost technique that accurately detects diagonal edges and predicts the value of pixels to be encoded based on the gradients available within the standard predictive template of JPEG-LS. We provide experimental results to show that the proposed technique outperforms JPEG-LS in terms of predicted mean squared error, by a margin ofup to 8.5 1%

A neighbourhood analysis based technique for real-time error concealment in H.264 intra pictures

H.264s extensive use of context-based adaptive binary arithmetic or variable length coding makes streams highly susceptible to channel errors, a common occurrence over networks such as those used by mobile devices. Even a single bit error will cause a decoder to discard all stream data up to the next fixed length resynchronisation point, the worst scenario is that an entire slice is lost. In cases where retransmission and forward error concealment are not possible, a decoder should conceal any erroneous data in order to minimise the impact on the viewer. Stream errors can often be spotted early in the decode cycle of a macroblock which if aborted can provide unused processor cycles, these can instead be used to conceal errors at minimal cost, even as part of a real time system. This paper demonstrates a technique that utilises Sobel convolution kernels to quickly analyse the neighbourhood surrounding erroneous macroblocks before performing a weighted multi-directional interpolation. This generates significantly improved statistical (PSNR) and visual (IEEE structural similarity) results when compared to the commonly used weighted pixel value averaging. Furthermore it is also computationally scalable, both during analysis and concealment, achieving maximum performance from the spare processing power available

Segmenting Oil Spills from Blurry Images Based on Alternating Direction Method of Multipliers

We exploit the alternating direction method of multipliers (ADMM) for developing an oil spill segmentation method, which effectively detects oil spill regions in blurry synthetic aperture radar (SAR) images. We commence by constructing energy functionals for SAR image deblurring and oil spill segmentation separately. We then integrate the two energy functionals into one overall energy functional subject to a linear mapping constraint that correlates the deblurred image and the segmentation indicator. The overall energy functional along with the linear constraint follows the form of alternating direction method of multipliers and thus enables an effective augmented Lagrangian optimization. Furthermore, the iterative updates in the ADMM maintain information exchanges between the energy minimizations for SAR image deblurring and oil spill segmentation. Most existing blurry image segmentation strategies tend to consider deblurring and segmentation as two independent procedures with no interactions, and the operation of deblurring is thus not guided for obtaining accurate segmentation. In contrast, we integrate deblurring and segmentation into one overall energy minimization framework with information exchanges between the two procedures. Therefore, the deblurring procedure is inclined to operate in favor of more accurate oil spill segmentation. Experimental evaluations validate that our framework outperforms the separate deblurring and segmentation strategy for detecting oil spill regions in blurry SAR images