Abstract
Since lossless compression can only achieve two to four times data compression, it may not be efficient to deploy lossless compression in bandwidth constrained applications. Instead, it would be more economical to adopt perceptually lossless compression, which can attain ten times or more compression without loss of important information. Consequently, one can transmit more images over bandwidth limited channels. In this research, we first aimed to compare and select the best compression algorithm in the literature to achieve a compression ratio of 0.1 and 40 dBs or more in terms of a performance metric known as human visual system model (HVSm) for maritime and sonar images. Our second objective was to demonstrate error concealment algorithms that can handle corrupted pixels due to transmission errors in interference-prone communication channels. Using four state-of-the-art codecs, we demonstrated that perceptually lossless compression can be achieved for realistic maritime and sonar images. At the same time, we also selected the best codec for this purpose using four performance metrics. Finally, error concealment was demonstrated to be useful in recovering lost pixels due to transmission errors.
Highlights
Since the appearance of the Joint Photographic Experts Group (JPEG) standard in the 1990s, image compression has been well-developed [1,2,3,4,5,6]
About 15 years ago, there were some developments in discrete cosine transform (DCT) based algorithms, where overlapped blocks known as lapped transforms (LT) were used to further improve the compression [11]
According to the results found in [33], it was determined that two performance metrics known as human visual system (HVS) and HVS with masking (HVSm) correlate well with human perception
Summary
Since the appearance of the Joint Photographic Experts Group (JPEG) standard in the 1990s, image compression has been well-developed [1,2,3,4,5,6]. Lossless image compression algorithms include JPEG [7]. Some recent algorithms such as X264 (software implementation of H.264/AVC standard) [9] and X265 (software implementation of H.265/HEVC standard) [10] provide lossless compression options. JPEG, X264, and X265 are discrete cosine transform (DCT) based algorithms and JPEG2000 is wavelet based. About 15 years ago, there were some developments in DCT based algorithms, where overlapped blocks known as lapped transforms (LT) were used to further improve the compression [11]. In the past few years, a group of researchers at Xiph have incorporated LT [11]
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