This paper presents a novel system that makes effective use of High Dynamic Range (HDR) image data to improve and
maintain the best viewing quality of video broadcast on current mobile display devices. The proposed approach
combines bilateral filtering with an adaptive tone mapping method used to enable the enhancement of the perceptual
quality of the video frames at the display device. The bilateral filter separates the frame into large-scale and detail layers.
The large-scale layer is divided into bright, mid-tone and dark regions, which are each processed by an appropriate tone
mapping function. Ambient and backlight sensors at the display device provide information about current illumination
conditions, which are used to intelligently and dynamically vary the levels and thresholds of post-processing applied at
the decoder, thereby maintaining a constant level of perceived quality.
Block based texture synthesis algorithms have shown better results than others as they help to preserve the global structure. Previous research has proposed several approaches in the pixel domain, but little effort has been taken in the synthesis of texture in a multiresolution domain. We propose a multiresolution framework in which coefficient-blocks of the spatio-frequency components of the input texture are efficiently stitched together to form the corresponding components of the output texture. We propose two algorithms to this effect. In the first, we use a constant block size
throughout the algorithm. In the second, we adaptively split blocks so as to use the largest possible block size in order to preserve the global structure, while maintaining the mismatched error of the overlapped boundaries below a certain error tolerance. Special consideration is given to minimization of the computational cost, throughout the algorithm designs. We show that the adaptation of the multiresolution approach results in a fast, cost-effective, flexible texture synthesis algorithm that is capable of being used in modern, bandwidth-adaptive, real-time imaging applications. A collection of
regular and stochastic test textures is used to prove the effectiveness of the proposed algorithm.