Techniques to facilitate compression of depth data and real-time reconstruction of high-quality light fields. A parameter space of values for a line, pairs of endpoints on different sides of the line, and a palette index for each pixel of a pixel tile of a depth image is sampled. Values for the line, the pairs of endpoints, and the palette index that minimize an error are determined and stored.

Systems, devices, and methods for encoding information bits for storage, including encoding information bits and balance bits to obtain a first bit chunk of a first arrangement; permuting the first bit chunk to obtain a second bit chunk of a second arrangement; encoding the second bit chunk to obtain a third bit chunk of the second arrangement; permuting a first portion of the third bit chunk to obtain a fourth bit chunk of the first arrangement, and encoding the fourth bit chunk to obtain a fifth bit chunk of the first arrangement; permuting a second portion of the third bit chunk, and adjusting the balance bits based on the fifth bit chunk and the permutated second portion of the third bit chunk; adjusting the first arrangement based on the adjusted balance bits, and obtaining a codeword based on the adjusted first arrangement; and transmitting the codeword to a storage device.

Techniques are described for wireless communication. One method includes segmenting a payload into a plurality of code blocks; generating, for each code block, a cyclic redundancy check (CRC); encoding each code block and associated CRC in one or more codewords of a plurality of codewords; and transmitting the codewords. The encoding is based at least in part on a low density parity check code (LDPCC) encoding type. Another method includes receiving a plurality of codewords associated with a payload encoded using a LDPCC encoding type; decoding a set of the codewords associated with the first payload and a CRC; and transmitting one of an acknowledgement (ACK) or a non-acknowledgement (NAK) for the set of the codewords.

Systems and methods are provided for concatenated error-correcting coding. An apparatus may include a Low-Density Parity-Check (LDPC) decoder configured to perform an iterative LDPC decoding process on bits of an LDPC codeword, a Bose—Chaudhuri—Hocquenghem (BCH) decoder coupled to the LDPC decoder and a BCH scheduler coupled to the LDPC decoder and the BCH decoder. The LDPC codeword may be generated by LDPC encoding a Bose—Chaudhuri—Hocquenghem (BCH) codeword and the BCH codeword may be generated by BCH encoding a data unit. The BCH scheduler may be configured to determine whether a triggering condition for the BCH decoder is met after each iteration of the iterative LDPC decoding process and activate the BCH decoder to operate on an intermediate decoding result of the LDPC decoder if the triggering condition for the BCH decoder is met.

A storage device includes: a memory; and a processor configured to, at the time of writing data into the memory, generate a first check code common to a plurality of types of error correction codes from the data on the basis of a correlation relationship between the plurality of types of error correction codes, add the first check code to the data and write the data into the memory, convert the first check code into a second check code based on any one of the plurality of types of error correction codes at the time of reading the data from the memory, and perform error correction by using the second check code.

A channel encoding method in a communication or broadcasting system is provided. The channel encoding method includes reading a first sequence corresponding to a parity check matrix, converting the first sequence to a second sequence by applying a certain rule to a block size corresponding to a parity check matrix and the first sequence, and encoding information bits based on the second sequence. The block size has at least two different integer values.

Examples described herein utilize multi-layer neural networks to decode encoded data (e.g., data encoded using one or more encoding techniques). The neural networks have nonlinear mapping and distributed processing capabilities which are advantageous in many systems employing the neural network decoders. In this manner, neural networks described herein are used to implement error code correction (ECC) decoders.

A decoding method of low-density parity-check (LDPC) codes based on partial average residual belief propagation includes the following steps: S1: calculating a size of a cluster π in a protograph based on a code length m and a code rate of a target codeword; S2: pre-computing an edge residual r.sub.c.sub.i.sub..fwdarw.v.sub.j corresponding to each edge from a variable node to a check node in a check matrix H; S3: calculating, based on π, a partial average residual (PAR) value corresponding to each cluster in the check matrix H; S4: sorting m/π clusters in descending order of corresponding PAR values, and updating an edge with a largest edge residual in each cluster; S5: updating edge information m.sub.c.sub.i.sub..fwdarw.v.sub.i from a check node c.sub.i to a variable node v.sub.j, and then updating a log-likelihood ratio (LLR) value L(v.sub.j) of the variable node v.sub.j; and S6: after the updating, making a decoding decision.

A storage device includes: a memory; and a processor configured to, at the time of writing data into the memory, generate a first check code common to a plurality of types of error correction codes from the data on the basis of a correlation relationship between the plurality of types of error correction codes, add the first check code to the data and write the data into the memory, convert the first check code into a second check code based on any one of the plurality of types of error correction codes at the time of reading the data from the memory, and perform error correction by using the second check code.

A decoding method of low-density parity-check (LDPC) codes based on partial average residual belief propagation includes the following steps: S1: calculating a size of a cluster π in a protograph based on a code length m and a code rate of a target codeword; S2: pre-computing an edge residual r.sub.c.sub.i.sub..fwdarw.v.sub.j corresponding to each edge from a variable node to a check node in a check matrix H; S3: calculating, based on π, a partial average residual (PAR) value corresponding to each cluster in the check matrix H; S4: sorting m/π clusters in descending order of corresponding PAR values, and updating an edge with a largest edge residual in each cluster; S5: updating edge information m.sub.c.sub..fwdarw.v.sub.i from a check node c.sub.i to a variable node v.sub.j, and then updating a log-likelihood ratio (LLR) value L(v.sub.j) of the variable node v.sub.j; and S6: after the updating, making a decoding decision.