Recently, 360° content has emerged as a new method for offering real-life interaction. Ultra-high resolution 360° content is mapped to the two-dimensional plane to adjust to the input of existing generic coding standards for transmission. Man...
Remote sensing applications have gained in popularity in recent years, which has resulted in vast amounts of data being produced on a daily basis. Managing and delivering large sets of data becomes extremely difficult and resource demanding for the d...
A new hybrid transform for lossless image compression exploiting a discrete wavelet transform (DWT) and prediction is the main new contribution of this paper. Simple prediction is generally considered ineffective in conjunction with DWT but we applie...
A new approach is proposed for lossless raster image compression employing interpolative coding. A new multifunction prediction scheme is presented first. Then, interpolative coding, which has not been applied frequently for image compression, is exp...
Traditional JPEG series image compression algorithms have limitations in speed. To improve the storage and transmission of 14-bit/pixel images acquired by infrared line-scan detectors, a novel method is introduced for achieving high-speed and highly...
This paper presents a hardware efficient pixel-domain just-noticeable difference (JND) model and its hardware architecture implemented on an FPGA. This JND model architecture is further proposed to be part of a low complexity pixel-domain perceptual...
Lossless compression of medical images allows for rapid image data exchange and faithful recovery of the compressed data for medical image assessment. There are many useful telemedicine applications, for example in diagnosing conditions such as malar...
Traditionally, it has been assumed that the compression of the sign of wavelet coefficients is not worth the effort because they form a zero-mean process. However, several image encoders such as JPEG 2000 include sign-coding capabilities. In this pap...
Two key hurdles to the adoption of Machine Learning (ML) techniques in hyperspectral data compression are computational complexity and scalability for large numbers of bands. These are due to the limited computing capacity available in remote sensing...