Depth sequence coding with hierarchical partitioning and spatial-domain quantization

Depth coding in 3D-HEVC deforms object shapes due to block-level edge-approximation and lacks efficient techniques to exploit the statistical redundancy, due to the frame-level clustering tendency in depth data, for higher coding gain at near-lossless quality. This paper presents a standalone mono-view depth sequence coder, which preserves edges implicitly by limiting quantization to the spatial-domain and exploits the frame-level clustering tendency efficiently with a novel binary tree-based decomposition (BTBD) technique. The BTBD can exploit the statistical redundancy in frame-level syntax, motion components, and residuals efficiently with fewer block-level prediction/coding modes and simpler context modeling for context-adaptive arithmetic coding. Compared with the depth coder in 3D-HEVC, the proposed one has achieved significantly lower bitrate at lossless to near-lossless quality range for mono-view coding and rendered superior quality synthetic views from the depth maps, compressed at the same bitrate, and the corresponding texture frames. © 1991-2012 IEEE

Lossless image coding using hierarchical decomposition and recursive partitioning

State-Of-The-Art lossless image compression schemes, such as JPEG-LS and CALIC, have been proposed in the context-adaptive predictive coding framework. These schemes involve a prediction step followed by context-adaptive entropy coding of the residuals. However, the models for context determination proposed in the literature, have been designed using ad-hoc techniques. In this paper, we take an alternative approach where we fix a simpler context model and then rely on a systematic technique to efficiently exploit spatial correlation to achieve efficient compression. The essential idea is to decompose the image into binary bitmaps such that the spatial correlation that exists among non-binary symbols is captured as the correlation among few bit positions. The proposed scheme then encodes the bitmaps in a particular order based on the simple context model. However, instead of encoding a bitmap as a whole, we partition it into rectangular blocks, induced by a binary tree, and then independently encode the blocks. The motivation for partitioning is to explicitly identify the blocks within which the statistical correlation remains the same. On a set of standard test images, the proposed scheme, using the same predictor as JPEG-LS, achieved an overall bit-rate saving of 1.56% against JPEG-LS. © 2016 The Authors

Lossless hyperspectral image compression using binary tree based decomposition

A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing "original pixel intensity"based coding approaches using traditional image coders (e.g. JPEG) to the "residual" based approaches using a predictive coder exploiting band-wise correlation for better compression performance. Moreover, as HS images are used in detection or classification they need to be in original form; lossy schemes can trim off uninteresting data along with compression, which can be important to specific analysis purposes. A modified lossless HS coder is required to exploit spatial- spectral redundancy using predictive residual coding. Every spectral band of an HS image can be treated like they are the individual frame of a video to impose inter band prediction. In this paper, we propose a binary tree based lossless predictive HS coding scheme that arranges the residual frame into integer residual bitmap. High spatial correlation in HS residual frame is exploited by creating large homogeneous blocks of adaptive size, which are then coded as a unit using context based arithmetic coding. On the standard HS data set, the proposed lossless predictive coding has achieved compression ratio in the range of 1.92 to 7.94. In this paper, we compare the proposed method with mainstream lossless coders (JPEG-LS and lossless HEVC). For JPEG-LS, HEVCIntra and HEVCMain, proposed technique has reduced bit-rate by 35%, 40% and 6.79% respectively by exploiting spatial correlation in predicted HS residuals

Inherently Edge Preserving Depth Map Coding Using Context Adaptive Arithmetic Coding and Binary Tree Based Decomposition

There has been a growing research interest in 3D and free viewpoint video systems to enrich the immersive viewing experience of multimedia users. To efficiently support such video transmissions, a multi-view video plus depth coding architecture has recently been introduced using depth maps. Depth maps have distinct spatial and temporal characteristics and existing coding techniques are unable to exploit these characteristics fully in coding, which also affects the image quality of depth-image-based rendering. This thesis aims at designing an independent and autonomous depth map coder capable of exploiting unique depth characteristics to achieve high compression efficiency with better depth quality

Lossless image coding using binary tree decomposition of prediction residuals

State-of-the-art lossless image compression schemes, such as, JPEG-LS and CALIC, have been proposed in the context adaptive predictive coding framework. These schemes involve a prediction step followed by context adaptive entropy coding of the residuals. It can be observed that there exist significant spatial correlation among the residuals after prediction. The efficient schemes proposed in the literature rely on context adaptive entropy coding to exploit this spatial correlation. In this paper, we propose an alternative approach to exploit this spatial correlation. The proposed scheme also involves a prediction stage. However, we resort to a binary tree based hierarchical decomposition technique to efficiently exploit the spatial correlation. On a set of standard test images, the proposed scheme, using the same predictor as JPEG-LS, achieved an overall compression gain of 2.1% against JPEG-LS. © 2015 IEEE

Efficient coding of depth map by exploiting temporal correlation

With the growing demands for 3D and multi-view video content, efficient depth data coding becomes a vital issue in image and video coding area. In this paper, we propose a simple depth coding scheme using multiple prediction modes exploiting temporal correlation of depth map. Current depth coding techniques mostly depend on intra-coding mode that cannot get the advantage of temporal redundancy in the depth maps and higher spatial redundancy in inter-predicted depth residuals. Depth maps are characterized by smooth regions with sharp edges that play an important role in the view synthesis process. As depth maps are more sensitive to coding errors, use of transformation or approximation of edges by explicit edge modelling has impact on view synthesis quality. Moreover, lossy compression of depth map brings additional geometrical distortion to synthetic view. In this paper, we have demonstrated that encoding inter-coded depth block residuals with quantization at pixel domain is more efficient than the intra-coding techniques relying on explicit edge preservation. On standard 3D video sequences, the proposed depth coding has achieved superior image quality of synthesized views against the new 3D-HEVC standard for depth map bit-rate 0.25 bpp or higher

A novel depth motion vector coding exploiting spatial and inter-component clustering tendency

Motion vectors of depth-maps in multiview and free-viewpoint videos exhibit strong spatial as well as inter-component clustering tendency. This paper presents a novel coding technique that first compresses the multidimensional bitmaps of macroblock mode and then encodes only the non-zero components of motion vectors. The bitmaps are partitioned into disjoint cuboids using binary tree based decomposition so that the 0's and 1's are either highly polarized or further sub-partitioning is unlikely to achieve any compression. Each cuboid is entropy-coded as a unit using binary arithmetic coding. This technique is capable of exploiting the spatial and inter-component correlations efficiently without the restriction of scanning the bitmap in any specific linear order as needed by run-length coding. As encoding of non-zero component values no longer requires denoting the zero value, further compression efficiency is achieved. Experimental results on standard multiview test video sequences have comprehensively demonstrated the superiority of the proposed technique, achieving overall coding gain against the state-of-the-art in the range [22%, 54%] and on average 38%. © 2015 IEEE.2015 Visual Communications and Image Processing, VCIP 201

Inherently edge-preserving depth-map coding without explicit edge detection and approximation C3 - Proceedings - IEEE International Conference on Multimedia and Expo

In emerging 3D video coding, depth has significant importance in view synthesis, scene analysis, and 3D object reconstruction. Depth images can be characterized by sharp edges and smooth large regions. Most of the existing depth coding techniques use intra-coding mode and try to preserve edges explicitly with approximated edge modelling. However, edges can be implicitly preserved as long as the transformation is avoided. In this paper, we have demonstrated that inherent edge preserving encoding of inter-coded block residuals, uniformly quantized at pixel domain using motion data from associated texture components, is more efficient than explicitly edge preserving intra-coding techniques. Experimental results show that the proposed technique have achieved superior image quality of synthesized views against the new 3D-HEVC standard. Lossless applications of the proposed technique has achieved on average 66% and 23% bit-rate savings against 3D-HEVC with negligible quantization and perceptually unnoticeable view synthesis, respectively