RLFC: Random Access Light Field Compression using Key Views and Bounded Integer Encoding

We present a new hierarchical compression scheme for encoding light field images (LFI) that is suitable for interactive rendering. Our method (RLFC) exploits redundancies in the light field images by constructing a tree structure. The top level (root) of the tree captures the common high-level details across the LFI, and other levels (children) of the tree capture specific low-level details of the LFI. Our decompressing algorithm corresponds to tree traversal operations and gathers the values stored at different levels of the tree. Furthermore, we use bounded integer sequence encoding which provides random access and fast hardware decoding for compressing the blocks of children of the tree. We have evaluated our method for 4D two-plane parameterized light fields. The compression rates vary from 0.08 - 2.5 bits per pixel (bpp), resulting in compression ratios of around 200:1 to 20:1 for a PSNR quality of 40 to 50 dB. The decompression times for decoding the blocks of LFI are 1 - 3 microseconds per channel on an NVIDIA GTX-960 and we can render new views with a resolution of 512X512 at 200 fps. Our overall scheme is simple to implement and involves only bit manipulations and integer arithmetic operations.Comment: Accepted for publication at Symposium on Interactive 3D Graphics and Games (I3D '19

GST: GPU-decodable supercompressed textures

Modern GPUs supporting compressed textures allow interactive application developers to save scarce GPU resources such as VRAM and bandwidth. Compressed textures use fixed compression ratios whose lossy representations are significantly poorer quality than traditional image compression formats such as JPEG. We present a new method in the class of supercompressed textures that provides an additional layer of compression to already compressed textures. Our texture representation is designed for endpoint compressed formats such as DXT and PVRTC and decoding on commodity GPUs. We apply our algorithm to commonly used formats by separating their representation into two parts that are processed independently and then entropy encoded. Our method preserves the CPU-GPU bandwidth during the decoding phase and exploits the parallelism of GPUs to provide up to 3X faster decode compared to prior texture supercompression algorithms. Along with the gains in decoding speed, our method maintains both the compression size and quality of current state of the art texture representations

Efficient Random Access Compression Schemes for Image-Based Representations

In the last two decades, a new class of rendering methods called image-based rendering has emerged as an alternative to traditional geometry-based rendering methods. Image-based representations require an intensive amount of image data, making compression a critical issue. In particular, we need efficient compression schemes that map well to the current graphics hardware and provide random access capability for interactive applications. In this dissertation, we present new, efficient random-access compression schemes for three different image-based representations. Our first compression scheme, MPTC, introduces the idea of transcoding compressed video data directly to compressed textures such that the decoded output can be rendered using commodity texture mapping hardware. Our method improves rendering speeds up to an order of magnitude on mobile devices. Next, we propose two novel compression schemes, RLFC and HMLFC, for encoding light field images. In the RLFC algorithm, we describe a new hierarchical approach that provides progressive streaming and decoding capabilities that enable interactive rendering of light fields. HMLFC combines two different approaches for encoding light fields, motion compensation and hierarchical approaches to capture redundancies in a hybrid fashion; the compression rates with the hybrid approach improve by a factor of 2-5 times over the underlying motion and hierarchical schemes. Our third compression method, RANDM, deals with compressing depth maps using a range-partitioning and a global dictionary. RANDM is the first compression scheme that enables random access decoding for depth maps and achieves compression rates similar to the existing schemes. Each of these three methods allow compressed representation of image data in memory while rendering and selectively decoding required data quickly during run-time. We also present a novel, deep learning-based approach (neural networks), TexNN, for predicting the right set of encoding parameters for fast encoding of textures into compressed texture formats. Our method offers up to a magnitude of speed-up compared to prior compression algorithms while maintaining similar compression quality.Doctor of Philosoph