RLFC

Abstract

Figure 1: Our method (RLFC) is based on computing a hierarchy of new images: Representative Key Views (RKV) which capture the redundancies of the LF in the top levels of the hierarchy and Sparse Residual Views (SRV) that capture specific details of the LF in the bottom levels of the hierarchy. We present a visualization of the images (RKV & SRV) computed in RLFC hierarchy on a small sample grid of the LF for two datasets. RLFC achieves compression by removing the insignificant details and exploiting the sparsity of the SRV images in the hierarchy. ABSTRACTWe 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 512 × 512 at 200 fps. Our overall scheme is simple to implement and involves only bit manipulations and integer arithmetic operations. 1

Incremental Coding for Extractable Compression in the Context of Massive Random Access

“…The decoder then performs BP decoding taking into the previously received side information x j . From the rate adaptation performed at the encoder (19), the reconstruction is performed without any error.…”

“…IBEC vs C scheme: The C scheme shares similarities with the proposed IBEC scheme since, in both cases, a channel code is used to perform data encoding. In the IBEC scheme, the index of the previous request j is used to adapt the transmission and send the complement information only, as shown in (19). In the C scheme, this knowledge is not used.…”

“…For AI, MP schemes the coding of the source or the residue after prediction is done using an arithmetic coder. For C scheme, the encoding is done with the 6336 irregDeg2to21 LDPCA code available at [30] (as in (19), among the set of codes, we choose the LDPC code with minimum rate that allows perfect reconstruction of the vector).…”

“…For instance, separated compression of blocks of data, similar to the All Intra video compression, has been proposed for volumetric data [17] and digital power meter [18]. The idea to encode some data points as a reference, as in [11] [19], has been proposed for Internet of Things databases [20] and for the genomes [21]. However, these methods generally come with a significant transmission or storage rate penalty.…”