Processing architecture for digital camera has been built on JPEG2000 compression system. Concerns are to minimize processing power and data traffic inside (data-bandwidth at interface) and out-side (compression efficiency) of camera system. Key idea is to decompose Bayer matrix data given from image sensor into four half-resolution planes instead of interpolating to three full-resolution planes. With a new compression standard, JPEG2000, capable of handling multi-component image, the four-plane representation can be encoded into a single bit-stream. The representation saves data traffic between image reconstruction stage and compression stage by 1/3 to 1/2 compared to the Bayer-interpolated data. Not only reduced processing power prior to and during compression but also competitive or superior compression efficiency is achieved. On reconstruction to full resolution is Bayer-interpolation and/or edge-enhancement required as a post-processing to a standard decoder, while half or smaller resolution image is reconstructed without a post-processing. For mobile terminals with an integrated camera (image reconstruction in camera h/w and compression in terminal processor), this scheme helps to accommodate increased resolution with all the limited data-bandwidth from camera to terminal processor and limited processing capability.
This paper clarifies two traditional image size reduction and enlargement methods, projection-method (PM) and linear- interpolation (LI), in terms of multi-rate signal processing. We have proved that both size conversion methods of scaling ratio of N/M can be implemented with the same structure, i.e., zero-hold enlargement by 1:n, average filtering of M-taps for PM or N-taps for LI, and M:1 down- sampling for both methods. Such implementation enables objective evaluations of PM and LI under the same manner that evaluates other size conversion methods originally developed with digital filtering. The empirical matter that PM provides better image quality in reduction than LI and the opposite behavior in enlargement is confirmed under multi-rate signal processing. We have developed a unified method of PM and LI by introducing selective filter-length. N-taps for enlargement and M-taps for reduction are used to produce better or equal image quality at any scaling ratio of N/M than PM or LI alone does. We also show that minute filter-length adjustment of the unified method can control image quality more delicately; a slight decrease of the filter-length provides sharper images and a slight increase provides smoother images.
Wavelet transform has recently been attracting notable attention in its applicability for a variety of signal processing or image coding1'2'3, since it is expected that wavelet provides a unified interpretation to transform coding, hierarchical coding, and subband coding, all of which have ever been studied separately. It is also expected that wavelet transform is shown to be more advantageous than other image coding schemes because the wavelet coefficients represent the features of an image localized both in spatial and frequency domains4'5. In case of transform coding or subband coding, the efficiency is generally maximized by designing bit allocation to each decomposed band signal proportional to the relative importance of information in it. This technique is known as the optimum bit allocation algorithm (OBA). However, OBA should not directly be applied to the wavelet coding, because OBA does not well exploit the spatial local information represented on each wavelet coefficient. The purpose of this work is to develop a quantization scheme which maintains significant spatial information locally represented on wavelet coefficients. Preserving only the selected coefficients which represent visually significant features and discarding the others, is expected to keep high image quality since the significant features will be kept even at a low bit rate. In this respect, we propose two kinds of image data compression techniques employing a selective preservation of wavelet coefficients. Section 2 gives a brief description of wavelet transform, which includes construction of wavelet basis functions, feature extraction with wavelet, and multi-resolution property of wavelet. In Section 3, the first technique is proposed where resolution dependent thresholding is introduced to classify wavelet coefficients into significant or insignificant ones. In Section 4, the second technique is proposed where better performance can be achieved by further classifying significant coefficients with a multiresolution property of wavelet. Finally, summary and conclusions are provided in Section 5.
Conference Committee Involvement (4)
Digital Photography III
29 January 2007 | San Jose, CA, United States
Digital Photography II
16 January 2006 | San Jose, California, United States
Digital Photography
17 January 2005 | San Jose, California, United States
Sensors, Color, Cameras, and Systems for Digital Photography
19 January 2004 | San Jose, California, United States
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