Professor Marko Huhtanen from the University of Oulu has introduced a groundbreaking method for compressing images, combining traditional techniques to enhance both efficiency and flexibility. This innovative approach has been detailed in a recent publication in IEEE Signal Processing Letters and aims to address the limitations of current compression methods.
In the realm of digital photography and image storage, the JPEG format is ubiquitous. Many photographers also utilize RAW formats, which retain more information for post-processing. Conventional JPEG compression often retains only 10%–25% of the original data captured, leading to potential quality issues that vary by viewer perception. This challenge is common among anyone who captures or shares images digitally.
Innovative Compression Technique
Professor Huhtanen’s method employs a unique mathematical approach, manipulating images both horizontally and vertically using diagonal matrices to construct approximations in layers. This technique is inspired by a simplified version of Berlekamp’s switching game, adapted for continuous forms. Huhtanen remarked, “Image compression is a fundamental problem in imaging—how to pack an image into the smallest possible space for fast transmission and sharing.”
Current JPEG technology is based on a method developed approximately 50 years ago by Nazir Ahmed, an American professor of electrical and computer engineering. Ahmed’s approach relied on principal component analysis (PCA), although he was unable to implement it effectively. Instead, he opted for a simpler technique using the discrete cosine transform (DCT), which has since become a standard in image compression. “Scientific publishing involves a lot of randomness, and it is hard to predict what will ultimately be considered significant,” Huhtanen noted.
Bridging Two Approaches
The primary objective of image compression is to eliminate as much data as possible without compromising perceived quality. Huhtanen explained, “JPEG divides the image into 64 parts, compressing each using DCT. Mathematically, it is not very interesting, but it works excellently in practice.” PCA, on the other hand, was considered too complex and labor-intensive for widespread application in image compression.
In his research, Huhtanen has successfully integrated the strengths of both DCT and PCA, allowing for a more flexible algorithm that leverages the advantages of each method. While he refrains from speculating on the widespread applicability of his findings, he emphasizes that his work addresses a long-standing issue in the field.
Huhtanen compares digital image compression to converting a photograph into a “negative,” where essential information is extracted and rendered into a visible format. This analogy highlights how the recipient ultimately receives a compressed “negative form” of the image, which is then transformed back into a visible representation.
Enhanced Speed and Efficiency
As internet speeds vary, many users encounter slow loading times for images and websites. Huhtanen explained that his method allows for improved transmission speeds and more efficient data handling. “Individual components arrive through the channel, and the image sharpens as the compression is decompressed. If this can be done better than it is currently, image transfer speeds up and more information can be transmitted,” he said.
By compressing images into smaller data sizes, Huhtanen’s technique not only conserves storage space but also enhances computational speed. This method is particularly well-suited for parallel data processing, allowing for stepwise image reconstruction, which facilitates precise control and adjustments during compression. Additionally, the process is expected to save energy, making it a more sustainable option for digital imaging.
As digital imaging continues to evolve, Professor Huhtanen’s innovative approach may provide significant advancements in how images are compressed, stored, and transmitted, potentially reshaping practices in photography and beyond.
