Lossy-to-lossless 3D image coding through prior coefficient lookup tables

Abstract

Abstract-This paper describes a low-complexity, highefficiency, lossy-to-lossless 3D image coding system. The proposed system is based on a novel probability model for the symbols that are emitted by bitplane coding engines. This probability model uses partially reconstructed coefficients from previous components together with a mathematical framework that captures the statistical behavior of the image. An important aspect of this mathematical framework is its generality, which makes the proposed scheme suitable for different types of 3D images. The main advantages of the proposed scheme are competitive coding performance, low computational load, very low memory requirements, straightforward implementation, and simple adaptation to most sensors.

Stationary Probability Model for Microscopic Parallelism in JPEG2000

“…These image types belong to different fields and are captured with different sensors. As observed in other works [16], [17], [33], the data produced after transforming images of the same type with the same wavelet filter-bank are statistically similar. We exploit this fact to generate a single lookup table (LUT) for each image type.…”

Bitplane Image Coding With Parallel Coefficient Processing

“…The LUT is different for each image type since the probability model exploits the fact that the data produced after transforming images of the same type (e.g., natural, medical, etc.) with the same wavelet filter-bank are statistically similar [35], [37], [38]. A more in-depth study on this stationary probability model can be found in [33].…”

Context-Adaptive Binary Arithmetic Coding With Fixed-Length Codewords

“…Recently, a probability model that avoids the use of adaptive mechanisms has been introduced in [14], [15]. Its main idea is that the probability of the symbols can be estimated depending on the bitplane and the context in which they are emitted.…”