Douglas W. Couwenhoven

ADPCM for advanced LANDSAT downlink applications

Under funding from the Defense LANDSAT Program Office, a method was developed for compressing multispectral data. A rate-controlled adaptive differential pulse code modulation technique was developed with minimal complexity for downlink applications. This algorithm uses an adaptive 2-D and 3-D prediction of pixels. The difference between the predicted and original pixel is quantized with a locally adaptive quantizer. This technique has produced compression ratios of up to 2.5:1 losslessly and up to 5:1 with minimal visual image quality loss on LANDSAT and M-7 high resolution multispectral data. This paper describes the ADPCM algorithm and its impact on numerical, visual and machine exploitation performance.<<ETX>>

Improved error diffusion incorporating a visual model

This paper describes an improved error diffusion algorithm for the purpose of digitally halftoning images. In a first variation of the algorithm an error signal is calculated by the difference between a visually perceived input value and a visually perceived output value. This is accomplished by applying a causal visual blur function to both the input and output images. This approach has the advantage that it minimizes the appearance of worm artifacts in the output image while simultaneously eliminating the edge artifacts associated with a previous visual error diffusion algorithm. In a second variation of the improved error diffusion algorithm, a local image activity detector is used to adaptively modify the input and output blur filters. This allows the error diffusion parameters to be optimized for different types of image content.

Adaptive error diffusion algorithm incorporating a visual model

This paper describes an improved error diffusion algorithm for the purpose of digitally halftoning images. In one variation of the algorithm an error signal is calculated by the difference between a visually perceived input value and a visually perceived output value. This is accomplished by applying a causal visual blur function to both the input and output images. This approach has the advantage that it minimizes the appearance of worm artifacts in the output image, while simultaneously eliminating the edge artifacts associated with a previous visual error diffusion algorithm. In a second variation of the improved error diffusion algorithm, a local image activity detector is used to adaptively modify the input and output blur filters. This allows the error diffusion parameters to be optimized for different types of image content.