A recently developed data-compression method is an adaptive technique for coding quantized wavelet-transformed data, nominally as part of a complete image-data compressor. Unlike some other approaches, this method admits a simple implementation and does not rely on the use of large code tables.
A common data compression approach, particularly for images, is to perform a wavelet transform on the input data, and then losslessly compress a quantized version of the wavelet-transformed data. Under this compression approach, it is common for the quantized data to include long sequences, or "runs," of zeros.
The new coding method uses prefix-free codes for the nonnegative integers as part of an adaptive algorithm for compressing the quantized wavelet-transformed data by run-length coding. In the form of run-length coding used here, the data sequence to be encoded is parsed into strings consisting of some number (possibly 0) of zeros, followed by a nonzero value. The nonzero value and the length of the run of zeros are encoded. For a data stream that contains a sufficiently high frequency of zeros, this method is known to be more effective than using a single variable length code to encode each symbol. The specific prefix-free codes used are from two classes of variable-length codes: a class known as Golomb codes, and a class known as exponential-Golomb codes. The codes within each class are indexed by a single integer parameter.
The present method uses exponential-Golomb codes for the lengths of the runs of zeros, and Golomb codes for the nonzero values. The code parameters within each code class are determined adaptively on the fly as compression proceeds, on the basis of statistics from previously encoded values. In particular, a simple adaptive method has been devised to select the parameter identifying the particular exponential-Golomb code to use. The method tracks the average number of bits used to encode recent runlengths, and takes the difference between this average length and the code parameter. When this difference falls outside a fixed range, the code parameter is updated (increased or decreased). The Golomb code parameter is selected based on the average magnitude of recently encoded nonzero samples.
The coding method requires no floating-point operations, and more readily adapts to local statistics than other methods. The method can also accommodate arbitrarily large input values and arbitrarily long runs of zeros. In practice, this means that changes in the dynamic range or size of the input data set would not require a change to the compressor.
The algorithm has been tested in computational experiments on test images. A comparison with a previously developed algorithm that uses large code tables (generated via Huffman coding on training data) suggests that the data-compression effectiveness of the present algorithm is comparable to the best performance achievable by the previously developed algorithm.
This work was done by Aaron Kiely and Matthew Klimesh of Caltech for NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.techbriefs.com/tsp under the Information Sciences category.
The software used in this innovation is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (818) 393-2827. Refer to NPO-40490.
This Brief includes a Technical Support Package (TSP).
Improved Compression of Wavelet-Transformed Images
(reference NPO-40490) is currently available for download from the TSP library.
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Overview
The document titled "Improved Compression of Wavelet-Transformed Images" from NASA's Jet Propulsion Laboratory discusses advanced techniques for compressing images that have undergone wavelet transformation. The focus is on enhancing the efficiency of image coding through adaptive methods, particularly in the context of space-based applications where data integrity and compression are critical.
The document outlines various coding techniques, including runlength coding and variable length coding, which are employed to optimize the representation of image data. It emphasizes the limitations of traditional runlength coding when dealing with data that has short and infrequent runs of zeros. To address this, the authors propose a direct coding approach using variable length codes, specifically Golomb codes, which adaptively estimate parameters based on local statistics of the data.
Key findings indicate that the performance of the proposed adaptive coding technique can rival or even surpass the theoretical bounds of traditional methods, particularly at high bit rates. The document presents results from tests conducted on various images, including those from the AVHRR instrument and the Mars Pathfinder mission, demonstrating the effectiveness of the adaptive coding strategy in real-world scenarios.
The authors also discuss the importance of exploiting dependencies in the data to improve coding efficiency. By encoding both runlengths and sample magnitudes, the adaptive technique can achieve better performance than methods that rely solely on runlength coding. The document suggests that a hybrid approach, which switches between runlength and direct coding based on the frequency of zeros in the data, could further enhance compression performance.
Additionally, the document highlights the potential for future improvements through refinements in code parameter selection and context modeling. The adaptive nature of the proposed algorithm allows for flexibility in accommodating changes in image size and bit depth without requiring significant alterations to the coding technique.
In conclusion, this technical support package provides valuable insights into innovative image compression methods that leverage wavelet transformation and adaptive coding techniques. These advancements are particularly relevant for aerospace applications, where efficient data transmission and storage are paramount. The findings contribute to the broader field of image processing and compression, with implications for various technological and commercial applications.
