RESUMO
Measuring the impact of compression on the reconstruction quality of holograms remains a challenge. A public subjectively-annotated holographic data set that allows for testing the performance of compression techniques and quality metrics is presented, in addition to a subjective visual quality assessment methodology. Moreover, the performance of the quality assessment procedures is compared for holographic, regular 2D and light field displays. For these experiments, a double-stimulus, multi-perspective, multi-depth testing methodology was designed and implemented. Analysis of the quality scores indicated that in the absence of a suitable holographic display and under the presented test conditions, non-holographic displays can be deployed to display numerically reconstructed holograms for visual quality assessment tasks.
RESUMO
Compression of macroscopic digital holograms is a major research problem, which if unresolved will continue to limit the possible applications of holography in multimedia contexts. The quest of searching for the most suitable representation for compression is still an open problem. In this work, we study sparsification by the wave atom transform, introduced in 2006 by Demanet et al., and experiment on four large-scale representative diffuse macroscopic holograms while testing compressibility in the object plane, Fourier plane, and defocused plane representations, respectively. We demonstrate that it is a suitable nonadaptive, sparsifying transform for Fourier or defocused content, and by integration into the wave atom coding (WAC) method, we sketch a full-fledged codec for the compression of macroscopic holograms. WAC is compared to two variants of JPEG 2000, with equal complexity of coding tools, and the more recent High Efficiency Video Coding (H.265/HEVC). For Fourier and defocused holograms, WAC outperforms the JPEG 2000 variants by 0.9-7.9 dB Bjøntegaard-Delta peak signal to noise ratio, especially in the former case, while it is as good as or better than even H.265/HEVC for very deep computer-generated holograms, thus improving on existing approaches.
RESUMO
An increasing number of image processing applications require an automated quality prediction of the visual content as perceived by humans. Since, sparse coding is suggested to be an underlying strategy of the brain's neural system, it would be logical to assume that specific tasks like quality assessment also attempt to adhere to this strategy. However, existing perceptual quality predictors, often mimicking the different stages of the human visual system and deploying machine learning strategies, such as neural networks, rarely integrate the concept of sparse coding in their design. In this paper, we first investigate the validity of such assumption by performing an empirical analysis on the relation between the structural information of the scene-captured via sparseness significance-and perceptual quality. Subsequently, we propose a new approach to integrate the significance of sparse coding features in the future imagequality measure (IQM) designs. We utilize the Fourier transform as a case study, which leads to a new IQM called sparseness significance ranking measure (SSRM). This measure essentially deploys a Fourier basis for sparse coding, a ranking mechanism based upon the amplitudes of the sparse coefficients and subsequently a complex correlation metric that assesses the correspondence between the ranked coefficient amplitude profiles of the reference and the distorted image. Moreover, we introduce a new methodology, namely separation ratio analysis, to assess the prediction quality of individual features or quality predictors given a target perceptual quality. The quality predictions by the proposed SSRM show excellent compatibility with perceptual quality scores. A set of routine benchmarking experiments utilizing the LIVE and CSIQ, IVC and TID2008 databases indicates a highly competitive performance with state of the art IQMs. Moreover, it delivers this performance at a low computational cost.