Lossless video coding is required in the fields of archiving and editing digital cinema or digital broadcasting contents. This paper combines a discrete wavelet transform and adaptive inter/intra-frame prediction in the wavelet transform domain to create multiresolution lossless video coding. The multiresolution structure offered by the wavelet transform facilitates interchange among several video source formats such as Super High Definition (SHD) images, HDTV, SDTV, and mobile applications. Adaptive inter/intra-frame prediction is an extension of JPEG-LS, a state-of-the-art lossless still image compression standard. Based on the image statistics of the wavelet transform domains in successive frames, inter/intra frame adaptive prediction is applied to the appropriate wavelet transform domain.
This adaptation offers superior compression performance. This is achieved with low computational cost and no increase in additional information. Experiments on digital cinema test sequences confirm the effectiveness of the proposed algorithm.
This paper proposes pathological microscopic image compression schemes that suit lossless and progressive transmission. Because pathological microscopic images require very high resolution, they create heavy storage requirements and long transmission times. Image compression is desired to reduce these problems. First, we propose a lossless Karhunen-Loeve Transform (KLT) based on ladder networks. The proposed lossless KLT reduces inter-color redundancies which increases coding performance. Next we propose a progressive transmission algorithm by combining the lossless KLT and set partitioning in hierarchical trees (SPIHT) with the S+P transform. SPIHT is adopted to encode individual color-transformed components. By considering coding efficiency, the transmission bit rates of each encoded component are determined. The resulting algorithm gives high coding performance and has progressive transmission capability. When all transmitted data are decoded, decoding yields the original image. We demonstrate the performance of the proposed algorithm when applied to super high definition pathological microscopic images. All the images used in our tests have 2048x2048 pixels and 24 bits per pixel. It is shown that the coding performance of the proposed algorithm is superior to that of DCT-based JPEG with the RGB/YUV transform.
Lossless image coding that can recover original image from its compressed signal is required in the fields of medical imaging, fine arts, printing, and any applications demanding high image fidelity. MAR (Multiplicative Autoregressive) predictive coding is an efficient lossless compression scheme. In this method, prediction coefficients are fixed within the subdivided block-by-block image and cannot to be adopted to local statistics efficiently. Furthermore, side-information such as prediction coefficients must be transmitted to the decoder at each block. In this paper, we propose an improved MAR coding method based on image segmentation. The proposed MAR predictor can be adapted to local statistics of image efficiently. This coding method does not need transmit side- information to the decoder at each pixel. The effectiveness of the proposed model is shown through experiments using SHD images.
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