Iterative joint source channel decoding for H.264 compressed video transmission

In this thesis, the error resilient transmission of H.264 compressed video using Context-based Adaptive Binary Arithmetic Code (CABAC) as the entropy code is examined. The H.264 compressed video is convolutionally encoded and transmitted over an Additive White Gaussian Noise (AWGN) channel. Two iterative joint source-channel decoding schemes are proposed, in which slice candidates that failed semantic verification are exploited. The first proposed scheme uses soft values of bits produced by a soft-input soft-output channel decoder to generate a list of slice candidates for each slice in the compressed video sequence. These slice candidates are semantically verified to choose the best one. A new semantic checking method is proposed, which uses information from slice candidates that failed semantic verification to virtually check the current slice candidate. The second proposed scheme is built on the first one. This scheme also uses slice candidates that failed semantic verification but it uses them to modify soft values of bits at the source decoder before they are fed back into the channel decoder for the next iteration. Simulation results show that both schemes offer improvements in terms of subjective quality and in terms of objective quality using PSNR and BER as measures. Keywords: Video transmission, H.264, semantics, slice candidate, joint source-channel decoding, error resilienc

MAP Joint Source-Channel Arithmetic Decoding for Compressed Video

In order to have robust video transmission over error prone telecommunication channels several mechanisms are introduced. These mechanisms try to detect, correct or conceal the errors in the received video stream. In this thesis, the performance of the video codec is improved in terms of error rates without increasing overhead in terms of data bit rate. This is done by exploiting the residual syntactic/semantic redundancy inside compressed video along with optimizing the configuration of the state-of-the art entropy coding, i.e., binary arithmetic coding, and optimizing the quantization of the channel output. The thesis is divided into four phases. In the first phase, a breadth-first suboptimal sequential maximum a posteriori (MAP) decoder is employed for joint source-channel arithmetic decoding of H.264 symbols. The proposed decoder uses not only the intentional redundancy inserted via a forbidden symbol (FS) but also exploits residual redundancy by a syntax checker. In contrast to previous methods this is done as each channel bit is decoded. Simulations using intra prediction modes show improvements in error rates, e.g., syntax element error rate reduction by an order of magnitude for channel SNR of 7.33dB. The cost of this improvement is more computational complexity spent on the syntax checking. In the second phase, the configuration of the FS in the symbol set is studied. The delay probability function, i.e., the probability of the number of bits required to detect an error, is calculated for various FS configurations. The probability of missed error detection is calculated as a figure of merit for optimizing the FS configuration. The simulation results show the effectiveness of the proposed figure of merit, and support the FS configuration in which the FS lies entirely between the other information carrying symbols to be the best. In the third phase, a new method for estimating the a priori probability of particular syntax elements is proposed. This estimation is based on the interdependency among the syntax elements that were previously decoded. This estimation is categorized as either reliable or unreliable. The decoder uses this prior information when they are reliable, otherwise the MAP decoder considers that the syntax elements are equiprobable and in turn uses maximum likelihood (ML) decoding. The reliability detection is carried out using a threshold on the local entropy of syntax elements in the neighboring macroblocks. In the last phase, a new measure to assess performance of the channel quantizer is proposed. This measure is based on the statistics of the rank of true candidate among the sorted list of candidates in the MAP decoder. Simulation results shows that a quantizer designed based on the proposed measure is superior to the quantizers designed based on maximum mutual information and minimum mean square error

Enhanced quality reconstruction of erroneous video streams using packet filtering based on non-desynchronizing bits and UDP checksum-filtered list decoding

The latest video coding standards, such as H.264 and H.265, are extremely vulnerable in error-prone networks. Due to their sophisticated spatial and temporal prediction tools, the effect of an error is not limited to the erroneous area but it can easily propagate spatially to the neighboring blocks and temporally to the following frames. Thus, reconstructed video packets at the decoder side may exhibit significant visual quality degradation. Error concealment and error corrections are two mechanisms that have been developed to improve the quality of reconstructed frames in the presence of errors. In most existing error concealment approaches, the corrupted packets are ignored and only the correctly received information of the surrounding areas (spatially and/or temporally) is used to recover the erroneous area. This is due to the fact that there is no perfect error detection mechanism to identify correctly received blocks within a corrupted packet, and moreover because of the desynchronization problem caused by the transmission errors on the variable-length code (VLC). But, as many studies have shown, the corrupted packets may contain valuable information that can be used to reconstruct adequately of the lost area (e.g. when the error is located at the end of a slice). On the other hand, error correction approaches, such as list decoding, exploit the corrupted packets to generate several candidate transmitted packets from the corrupted received packet. They then select, among these candidates, the one with the highest likelihood of being the transmitted packet based on the available soft information (e.g. log-likelihood ratio (LLR) of each bit). However, list decoding approaches suffer from a large solution space of candidate transmitted packets. This is worsened when the soft information is not available at the application layer; a more realistic scenario in practice. Indeed, since it is unknown which bits have higher probabilities of having been modified during transmission, the candidate received packets cannot be ranked by likelihood. In this thesis, we propose various strategies to improve the quality of reconstructed packets which have been lightly damaged during transmission (e.g. at most a single error per packet). We first propose a simple but efficient mechanism to filter damaged packets in order to retain those likely to lead to a very good reconstruction and discard the others. This method can be used as a complement to most existing concealment approaches to enhance their performance. The method is based on the novel concept of non-desynchronizing bits (NDBs) defined, in the context of an H.264 context-adaptive variable-length coding (CAVLC) coded sequence, as a bit whose inversion does not cause desynchronization at the bitstream level nor changes the number of decoded macroblocks. We establish that, on typical coded bitstreams, the NDBs constitute about a one-third (about 30%) of a bitstream, and that the effect on visual quality of flipping one of them in a packet is mostly insignificant. In most cases (90%), the quality of the reconstructed packet when modifying an individual NDB is almost the same as the intact one. We thus demonstrate that keeping, under certain conditions, a corrupted packet as a candidate for the lost area can provide better visual quality compared to the concealment approaches. We finally propose a non-desync-based decoding framework, which retains a corrupted packet, under the condition of not causing desynchronization and not altering the number of expected macroblocks. The framework can be combined with most current concealment approaches. The proposed approach is compared to the frame copy (FC) concealment of Joint Model (JM) software (JM-FC) and a state-of-the-art concealment approach using the spatiotemporal boundary matching algorithm (STBMA) mechanism, in the case of one bit in error, and on average, respectively, provides 3.5 dB and 1.42 dB gain over them. We then propose a novel list decoding approach called checksum-filtered list decoding (CFLD) which can correct a packet at the bit stream level by exploiting the receiver side user datagram protocol (UDP) checksum value. The proposed approach is able to identify the possible locations of errors by analyzing the pattern of the calculated UDP checksum on the corrupted packet. This makes it possible to considerably reduce the number of candidate transmitted packets in comparison to conventional list decoding approaches, especially when no soft information is available. When a packet composed of N bits contains a single bit in error, instead of considering N candidate packets, as is the case in conventional list decoding approaches, the proposed approach considers approximately N/32 candidate packets, leading to a 97% reduction in the number of candidates. This reduction can increase to 99.6% in the case of a two-bit error. The method’s performance is evaluated using H.264 and high efficiency video coding (HEVC) test model software. We show that, in the case H.264 coded sequence, on average, the CFLD approach is able to correct the packet 66% of the time. It also offers a 2.74 dB gain over JM-FC and 1.14 dB and 1.42 dB gains over STBMA and hard output maximum likelihood decoding (HO-MLD), respectively. Additionally, in the case of HEVC, the CFLD approach corrects the corrupted packet 91% of the time, and offers 2.35 dB and 4.97 dB gains over our implementation of FC concealment in HEVC test model software (HM-FC) in class B (1920×1080) and C (832×480) sequences, respectively