Fast Lossless Depth Image Compression

Wilson, Andrew

doi:10.1145/3132272.3134144

Cited by 32 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The approach saves more than 55% bitrate with a significant reduction in the coding complexity. Andrew D. Wilson [43] presented a lossless image compression method for 16-bit single channel images typical of Kinect depth cameras. The algorithm is faster than existing lossless techniques.…”

Section: A Edge-preserving Depth-map Codingmentioning

confidence: 99%

A Flexible Lossy Depth Video Coding Scheme Based on Low-rank Tensor Modelling and HEVC Intra Prediction for Free Viewpoint Video

Sharma¹,

Kumar²

2021

Preprint

View full text Add to dashboard Cite

The compression quality losses of depth sequences determine quality of view synthesis in free-viewpoint video. The depth map intra prediction in 3D extensions of the HEVC applies intra modes with auxiliary depth modeling modes (DMMs) to better preserve depth edges and handle motion discontinuities. Although such modes enable high efficiency compression, but at the cost of very high encoding complexity. Skipping conventional intra coding modes and DMMs in depth coding limits practical applicability of the HEVC for 3D display applications. In this paper, we introduce a novel low-complexity scheme for depth video compression based on low-rank tensor decomposition and HEVC intra coding. The proposed scheme leverages spatial and temporal redundancy by compactly representing the depth sequence as a high-order tensor. Tensor factorization into a set of factor matrices following CANDECOMP/PARAFAC (CP) decomposition via alternating least squares give a low-rank approximation of the scene geometry. Further, compression of factor matrices with HEVC intra prediction support arbitrary target accuracy by flexible adjustment of bitrate, varying tensor decomposition ranks and quantization parameters. The results demonstrate proposed approach achieves significant rate gains by efficiently compressing depth planes in low-rank approximated representation. The proposed algorithm is applied to encode depth maps of benchmark "Ballet" and "Breakdancing" sequences. The decoded depth sequences are used for view synthesis in a multi-view video system, maintaining appropriate rendering quality.

show abstract

Section: A Edge-preserving Depth-map Codingmentioning

confidence: 99%

A Flexible Lossy Depth Video Coding Scheme Based on Low-rank Tensor Modelling and HEVC Intra Prediction for Free Viewpoint Video

Sharma¹,

Kumar²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…RoomAlive refined the calibration of multiple projectors and depth cameras as well as the rendering of interactive projection mapped experiences [9,17]. RoomAlive has been shown to scale to as many as eight cameras in a conference room [31]. Lindlbauer, et al, uses a voxel grid representation to encode annotations throughout the physical environment [12].…”

Section: Related Workmentioning

confidence: 99%

Combating the Spread of Coronavirus by Modeling Fomites with Depth Cameras

Wilson

2020

Proc. ACM Hum.-Comput. Interact.

Self Cite

View full text Add to dashboard Cite

Fig. 1. From left to right: Using Azure Kinect body tracking to detect face touching; 3D reconstruction of room with two calibrated depth cameras; 3D contamination map; rendering of social distance calculations (green) and joints marked as contaminated (red spheres). Coronavirus is thought to spread through close contact from person to person. While it is believed that the primary means of spread is by inhaling respiratory droplets or aersols, it may also be spread by touching inanimate objects such as doorknobs and handrails that have the virus on it ("fomites"). The Centers for Disease Control and Prevention (CDC) therefore recommends individuals maintain "social distance" of more than six feet between one another. It further notes that an individual may be infected by touching a fomite and then touching their own mouth, nose or possibly their eyes. We propose the use of computer vision techniques to combat the spread of coronavirus by sounding an audible alarm when an individual touches their own face, or when multiple individuals come within six feet of one another or shake hands. We further propose using depth cameras to track where people touch parts of their physical environment throughout the day, and a simple model of disease spread among potential fomites. Projection mapping techniques can be used to display likely fomites in realtime, while headworn augmented reality systems can be used by custodial staff to perform more effective cleaning of surfaces. Such techniques may find application in particularly vulnerable settings such as schools, long-term care facilities and physician offices. CCS Concepts: • Human-centered computing → Interactive systems and tools.

show abstract

“…The depth stream was encoded using the RVL algorithm proposed by [33], a combination of run length encoding and variable length encoding compressing each frame independently using the process described in the paper.…”

Section: Visualizationmentioning

confidence: 99%

“…The normal and color streams are decoded normally as video streams (FFmpeg and NVENC were used in our case), updating pixel buffers at 30fps, which will be available in texture memory by the vertex shader. Depth data is decoded using the RVL algorithm [33] and updated into an array buffer which is aligned with UV coordinates in a vertex array passed onto the vertex shader.…”

Section: Visualizationmentioning

confidence: 99%

Three-Dimensional Visualization of Movement Qualities in Contemporary Dance

Anjos

Ribeiro

Fernandes

2018

Proceedings of the 5th International Conference on Movement and Computing

View full text Add to dashboard Cite

Analyzing and documenting contemporary dance movement data has proven to be a very difficult task due to the vast existence of styles and aesthetics which are characteristic of each individual choreographer. Indeed, identifying movement qualities with the intention of co-relating them to specific meanings and choreographic intentions is practically impossible without taking an artist-driven approach. Moreover, equipping dancers with wearable technology in order to accurately track each of their movements is only applicable in controlled laboratory scenarios, therefore not being a viable approach to analyze large scale real-world rehearsals or performances. This paper describes a visualization system that exposes movement qualities for dance through rendering effects. Combining optical flow analysis with depth video information, we were able to estimate the three-dimensional flow for each point in the cloud. This information was used to color point cloud videos with this information, showing potential to be used for the creation of different rendering effects, which are also discussed in this paper. The data used was captured in a non-intrusive manner during the creation process of a dance piece by Portuguese choreographer Rui Lopes Graça, enabling us to examine nuances and details of the rehearsals' process, which would not be replicable in a laboratory environment.

show abstract

Fast Lossless Depth Image Compression

Cited by 32 publications

References 9 publications

A Flexible Lossy Depth Video Coding Scheme Based on Low-rank Tensor Modelling and HEVC Intra Prediction for Free Viewpoint Video

A Flexible Lossy Depth Video Coding Scheme Based on Low-rank Tensor Modelling and HEVC Intra Prediction for Free Viewpoint Video

Combating the Spread of Coronavirus by Modeling Fomites with Depth Cameras

Three-Dimensional Visualization of Movement Qualities in Contemporary Dance

Contact Info

Product

Resources

About