Progressive Refinement Method Research Articles

Distributed video coding with progressive correlation noise refinement and maximum likelihood pre-decoding

Distributed video coding (DVC) reduces the complexity at the encoder by using source statistics at the decoder. However, a performance gap still exists between DVC and hybrid video coders due to the inaccuracy of correlation noise (CN) modeling and inefficient exploitation of side information. We propose improvements to the performance of DVC using the two following aspects. Firstly, a progressive refinement method is proposed to improve the accuracy of CN modeling for transform domain Wyner-Ziv video coding, in which previously decoded bitplanes are exploited to progressively refine estimated CN as bitplane decoding proceeds. Secondly, a maximum likelihood pre-decoding method is also proposed to obtain a further reduction in bitrate. In our method, bitplanes are pre-decoded first using the conditional bit probability without syndrome bits. The bitrate is reduced by avoiding requests for syndrome bits for the bitplanes having strong temporal correlation. The experimental results show that our proposed methods could provide bitrate savings up to 8.26% and PSNR gains up to 0.2 dB without a significant increase in decoding complexity.

Thermal radiation in a room: an improved progressive refinement method

A new approach is described for the calculation of radiative heat transfer within buildings. The progressive refinement method together and a fast view factor estimation algorithm are introduced, implemented and tested. Results obtained by the Improved Progressive Refinement Method (IPRM) are compared with the results from the standard matrix method and an earlier study. This paper also demonstrates how the IPRM is capable of evaluating the distribution of heat flux on wall surfaces iteratively to a desired accuracy level, and with no need to store view factors between all sub-surfaces concurrently. In other words, IPRM allows a more realistic evaluation of nonuniform thermal radiation of a space into a desired accuracy level, while requiring less computer time and memory.

The Close Objects Buffer: A Sharp Shadow Detection Technique for Radiosity Methods

Abstract Detecting sharp illumination variations such as shadow boundaries is an important problem for radiosity methods. Such illumination variations are captured using a nonuniform mesh that refines the areas exhibiting high illumination gradients. Nonuniform meshing techniques like discontinuity meshing often rely on shadow casting, and as a result their application is computationally expensive. This paper presents a sharp shadow detection technique for radiosity tools based on the progressive refinement method. The presented technique offers good results (especially for capturing sharp shadows cast by small “detail” objects), is very simple to implement, has negligible time and space requirements, and integrates well with other adaptive subdivision strategies in a radiosity tool based on progressive refinement.

Form Factor Calculation: a New Expression with Implementations on a Parallel T.Node Computer

AbstractThe radiosity technique uses the heat transfer theory to define a view‐independent illumination model. For complex scenes, calculation of this model is very time‐intensive, due largely to the determination of a geometric factor, defined with a double area integral, and named form factor. This paper presents a new expression of the form factor, which can be reformulated, in a computationally more efficient manner, as a simple line integral for planar polygonal convex surfaces that are perfectly diffuse.Recently, parallel computers have been used to decrease calculation time. Previous work is focused mainly on distributed implementations of a method, the progressive refinement method, widely used for complex environments. The T.Node is a completely reconfigurable MIMD machine, based on the transputer chip. In this paper, we compare a parallel implementation of the progressive refinement method with a direct method which takes into account the memory capabilities of this kind of machine.

Improving radiosity solutions through the use of analytically determined form-factors

Current radiosity methods rely on the calculation of geometric factors, known as form-factors, which describe energy exchange between pairs of surfaces in the environment. The most computationally efficient method for form-factor generation is a numerical technique known as the hemi-cube algorithm. Use of the hemi-cube is based on assumptions about the geometry of the surfaces involved. First, this paper examines the types of errors and visual artifacts that result when these assumptions are violated. Second, the paper shows that these errors occur more frequently in progressive refinement radiosity than in the originally proposed full matrix radiosity solution. Next, a new analytical technique for determining form-factors that is immune to the errors of the hemi-cube algorithm is introduced. Finally, a hybrid progressive refinement method that invokes the new technique to correctly compute form-factors when hemi-cube assumptions are violated is presented.