Video encoding algorithms require processing of data arranged in blocks of pixels. For efficient computation, pixel
blocks are expected to be stored contiguously in memory, and within each block, pixels are to be arranged in a raster
scan (left to right, top to bottom order). Since data captured from the video port is linearly arranged in memory (one line
after the other), it is necessary to arrange the data in the two-dimensional form before processing for encoding. A
common approach to achieve the two-dimensional arrangement is through optimized functions (in C or Assembly) to
arrange the captured data, which is stored in an intermediate buffer, into an input buffer from which it is ready for
encoding. However, this approach has two main drawbacks. First, a portion of the CPU MHZ budget is consumed only
on data arrangement. Second, an intermediate data buffer is required to hold the data before the arrangement into the
input buffer takes place, and hence increasing the memory requirements. In this paper, a memory and MHZ efficient
EDMA transfer scheme is introduced for simultaneous data transfer and two-dimensional arrangement from the video
port to the DSP memory. The proposed scheme is described in details for TI TMS320DM642<sup>TM</sup>.
This paper introduces a new image super-resolution algorithm in an adaptive, robust M-estimation framework. Super-resolution
reconstruction is formulated as an optimization (minimization) problem whose objective function is based on a robust error norm. The effectiveness of the proposed scheme lies in the selection of a specific class of robust M-estimators, <i>redescending M-estimators</i>, and the incorporation of a similarity measure to adapt the estimation process to each of the low-resolution frames. Such a choice helps in dealing with violations to the assumed imaging model that could have generated the low-resolution frames from the unknown high-resolution one. The proposed approach effectively suppresses the outliers without the use of regularization in the objective function, and results in high-resolution images with crisp details and no artifacts. Experiments on both synthetic and real sequences demonstrate the superior performance over methods based on the L<sub>2</sub> and L<sub>1</sub> in the objective function.