A Comparison of Compression Codecs for Maritime and Sonar Images in Bandwidth Constrained Applications

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.

Single Space Object Image Denoising and Super-Resolution Reconstructing Using Deep Convolutional Networks

“…However, in this article, SSIM was probably not a good metric as the numbers were so close in different methods up to the fifth or sixth decimal places (as illustrated in Table 2). So the human visual system (HVS) [37] and human visual system model (HVSm) [37] metrics are also used to compare the reconstruction performance among different methods.…”

Perceptually Lossless Compression with Error Concealment for Periscope and Sonar Videos

“…Originally, our sponsor required us to achieve perceptually lossless compression with 8:1 compression ratio for videos at frame rate of 6 frames per second. In our companion paper [11], we have demonstrated that 10 to 1 perceptually lossless compression can be achieved for still images. Hence, we aim at 20 to 1 compression for videos in this research, as videos have more redundancy than still images.…”

“…In our earlier study [11], we proposed an error concealment scheme for still image compression. It was observed that lost information can be recovered via error concealment algorithms.…”

A lossless compression method for multi-component medical images based on big data mining

“…The image compression methods based on neural network 36 , 37 introduce the concept of learning into this filed, possessing good performance. Perceptually lossless compression 38 , 39 can attain higher compression performance without loss of important information and has good application potential over bandwidth limited channels.…”