Subsequent Decoding Research Articles

Peniaphobia as a Component of the Destructive Digital Media Discourse in the Asia-Pacific Region

Modern hybrid linguistic-mental and information warfare massively uses the promotion of destruction in the mass media as a tool of negative ‘soft power’ media discourse directed against China, Vietnam and Russia owing to their being the key players in the geopolitical arena of Southeast Asia. The discourse of Western geopolitical opponents promotes peniaphobia (fear of poverty), which is one of the basic human fears since it is associated with ensuring the efficient viability of man and human civilization as a whole. The authors attribute peniaphobia to one of the universal social stigmas that affect the social structure and culture of the community, recognizing that media discourse is actively using open public Internet communication to control public consciousness gently. Meanwhile, intense exploitation by the media of alleged financial insolvency, looming economic crises, food shortages and impending hunger, the adverse effects of international labor migration and the deplorable state of the labor markets rapidly generate protest moods in society, changing social perceptions of money, food and food consumption. Information intakes that enhance peniaphobia use scenario modeling the presentation of potentially dangerous themes in the mass media of Russia, Vietnam and China. The article assesses the manipulation of public consciousness in the context of the pandemic, complicated by the psychological and informational confrontation between East and West and Western socio-economic and political sanctions imposed in connection with the Russian-Ukrainian conflict. Modern social culture has updated the features of presenting information using a digital format and subsequent decoding, which, in general, has influenced social dynamics. Based on analyzing the ethno- and sociocultural behavior of ‘digital aborigines’ on the Internet, the sociocultural approach to modern manipulation techniques of the public consciousness demonstrates the change in the use of methods of manipulation compared to representatives of the ‘pre-digital generation’.

FEBE-Net: Feature Exploration Attention and Boundary Enhancement Refinement Transformer Network for Bladder Tumor Segmentation

The automatic and accurate segmentation of bladder tumors is a key step in assisting urologists in diagnosis and analysis. At present, existing Transformer-based methods have limited ability to restore local detail features and insufficient boundary segmentation capabilities. We propose FEBE-Net, which aims to effectively capture global and remote semantic features, preserve more local detail information, and provide clearer and more precise boundaries. Specifically, first, we use PVT v2 backbone to learn multi-scale global feature representations to adapt to changes in bladder tumor size and shape. Secondly, we propose a new feature exploration attention module (FEA) to fully explore the potential local detail information in the shallow features extracted by the PVT v2 backbone, eliminate noise, and supplement the missing fine-grained details for subsequent decoding stages. At the same time, we propose a new boundary enhancement and refinement module (BER), which generates high-quality boundary clues through boundary detection operators to help the decoder more effectively preserve the boundary features of bladder tumors and refine and adjust the final predicted feature map. Then, we propose a new efficient self-attention calibration decoder module (ESCD), which, with the help of boundary clues provided by the BER module, gradually and effectively recovers global contextual information and local detail information from high-level features after calibration enhancement and low-level features after exploration attention. Extensive experiments on the cystoscopy dataset BtAMU and five colonoscopy datasets have shown that FEBE-Net outperforms 11 state-of-the-art (SOTA) networks in segmentation performance, with higher accuracy, stronger robust stability, and generalization ability.

Feature differences reduction and specific features preserving network for RGB-T salient object detection

In RGB-T salient object detection, effective utilization of the different characteristics of RGB and thermal modalities is essential to achieve accurate detection. Most of the previous methods usually only focus on reducing the differences between modalities, which may ignore the specific features that are crucial for salient object detection, leading to suboptimal results. To address the above issue, an RGB-T SOD network that simultaneously considers the reduction of modality differences and the preservation of specific features is proposed. Specifically, we construct a modality differences reduction and specific features preserving module (MDRSFPM) which aims to bridge the gap between modalities and enhance the specific features of each modality. In MDRSFPM, the dynamic vector generated by the interaction of RGB and thermal features is used to reduce modality differences, and then a dual branch is constructed to deal with the RGB and thermal modalities separately, employing a combination of channel-level and spatial-level operations to preserve their respective specific features. In addition, a multi-scale global feature enhancement module (MGFEM) is proposed to enhance global contextual information to provide guidance information for the subsequent decoding stage, so that the model can more easily localize the salient objects. Furthermore, our approach includes a fully fusion and gate module (FFGM) that utilizes dynamically generated importance maps to selectively filter and fuse features during the decoding process. Extensive experiments demonstrate that our proposed model surpasses other state-of-the-art models on three publicly available RGB-T datasets remarkably. Our code will be released at https://github.com/JOOOOKII/FRPNet.

Reconstructing and analyzing the invariances of low-dose CT image denoising networks.

Deep learning-based methods led to significant advancements in many areas of medical imaging, most of which are concerned with the reduction of artifacts caused by motion, scatter, or noise. However, with most neural networks being black boxes, they remain notoriously difficult to interpret, hindering their clinical implementation. In particular, it has been shown that networks exhibit invariances w.r.t. input features, that is, they learn to ignore certain information in the inputdata. To improve the interpretability of deep learning-based low-dose CT image denoisingnetworks. We learn a complete data representation of low-dose input images using a conditional variational autoencoder (cVAE). In this representation, invariances of any given denoising network are then disentangled from the information it is not invariant to using a conditional invertible neural network (cINN). At test time, image-space invariances are generated by applying the inverse of the cINN and subsequent decoding using the cVAE. We propose two methods to analyze sampled invariances and to find those that correspond to alterations of anatomicalstructures. The proposed method is applied to four popular deep learning-based low-dose CT image denoising networks. We find that the networks are not only invariant to noise amplitude and realizations, but also to anatomicalstructures. The proposed method is capable of reconstructing and analyzing invariances of deep learning-based low-dose CT image denoising networks. This is an important step toward interpreting deep learning-based methods for medical imaging, which is essential for their clinical implementation.

Introducing bidirectional attention for autoregressive models in abstractive summarization

Abstractive summarization methods typically follow the autoregressive paradigm using the causal masks in the decoder for training and inference efficiency. However, this approach leads to a constant context throughout the generation process, which conflicting with the bidirectional characteristics of natural language. Although previous attempts have been made to incorporate bidirectional attention in the decoding process through non-autoregressive approach, the evaluation results are not comparable to the autoregressive methods. To bring bidirectional attention to the autoregressive process while maintaining superior performance, we propose the global autoregressive paradigm, which takes the outputs of the autoregressive process as additional inputs in the subsequent global iteration. Specifically, we build a bidirectional decoder alongside the original encoder and decoder to capture the bidirectional context of the outputs. This context is updated after each autoregressive decoding iteration. The decoder then integrates the updated context into subsequent autoregressive decoding steps, enhancing the generative process with a more comprehensive and authentic context. Additionally, we use contrastive learning to train the model to extract reliable features from the bidirectional context and apply reinforcement learning to improve the model's utilization of this context. We evaluate our method on CNN/DM, XSum, and NYT datasets, and the results highlight the significance of the bidirectional context. Our method achieves the best performance in terms of ROUGE-2 on CNN/DM (23.96), and performs comparably on XSum (25.45) and NYT (27.91). It also outperforms all the baselines in terms of BERTScore, with a score of 89.96 on CNN/DM, 92.70 on XSum, and 90.04 on NYT. Furthermore, our method can perform better with a larger beam size.

MPOCSR: optical chemical structure recognition based on multi-path Vision Transformer

AbstractOptical chemical structure recognition (OCSR) is a fundamental and crucial task in the field of chemistry, which aims at transforming intricate chemical structure images into machine-readable formats. Current deep learning-based OCSR methods typically use image feature extractors to extract visual features and employ encoder-decoder architectures for chemical structure recognition. However, the performance of these methods is limited by their image feature extractors and the class imbalance of elements in chemical structure representation. This paper proposes MPOCSR (multi-path optical chemical structure recognition), which introduces the multi-path Vision Transformer (MPViT) and the class-balanced (CB) loss function to address these two challenges. MPOCSR uses MPViT as an image feature extractor, combining the advantages of convolutional neural networks and Vision Transformers. This strategy enables the provision of richer visual information for subsequent decoding processes. Furthermore, MPOCSR incorporates CB loss function to rebalance the loss weights among different categories. For training and validation of our method, we constructed a dataset that includes both Markush and non-Markush structures. Experimental results show that MPOCSR achieves an accuracy of 90.95% on the test set, surpassing other existing methods.

Dual-Prior Augmented Decoding Network for Long Tail Distribution in HOI Detection

Human object interaction detection aims at localizing human-object pairs and recognizing their interactions. Trapped by the long-tailed distribution of the data, existing HOI detection methods often have difficulty recognizing the tail categories. Many approaches try to improve the recognition of HOI tasks by utilizing external knowledge (e.g. pre-trained visual-language models). However, these approaches mainly utilize external knowledge at the HOI combination level and achieve limited improvement in the tail categories. In this paper, we propose a dual-prior augmented decoding network by decomposing the HOI task into two sub-tasks: human-object pair detection and interaction recognition. For each subtask, we leverage external knowledge to enhance the model's ability at a finer granularity. Specifically, we acquire the prior candidates from an external classifier and embed them to assist the subsequent decoding process. Thus, the long-tail problem is mitigated from a coarse-to-fine level with the corresponding external knowledge. Our approach outperforms existing state-of-the-art models in various settings and significantly boosts the performance on the tail HOI categories. The source code is available at https://github.com/PRIS-CV/DP-ADN.

Proof of Concept of the Use of the Parametric Effect in Two Media with Application to Underwater Acoustic Communications

Nonlinear acoustics offers a new range of acoustic applications that are currently being exploited. The parametric nonlinear effect—the occurrence of low frequencies with modulated high-frequency emission—is of particular interest. This work provides a systematic exposition of the theoretical framework on which the so-called parametric nonlinear effect is based. In relation to this behavior is an analytical discussion of how to solve the problem for two cases: (i) nonlinear behavior with modulation, and (ii) parametric emission of two monochromatic waves (bi-frequency). Subsequently, parametric emission experiments were carried out in air and water using the same transducer to compare the results with those obtained theoretically. In this sense, directivity and attenuation measurements are obtained. Conclusively, this research offers a proof of concept for underwater acoustic communications. It is characterized by the transmission of a binary sequence through Frequency Shift Keying (FSK) modulation, and the subsequent decoding of each received bit (either 1 or 0) utilizing advanced signal processing with the cross-correlation technique. This paper accentuates the significant potential of employing the parametric effect for specialized communication applications.

Preliteracy Skills Mediate the Relation Between Early Speech Sound Production and Subsequent Reading Outcomes.

Learning to read is a complex, multifaceted process that relies on several speech and language-related subskills. Individual differences in word reading outcomes are indicated among children with inaccurate speech sound productions, with some of these children developing later reading difficulties. There are inconsistent reports as to whether phonological deficits and/or weaknesses in oral language explain these subsequent reading difficulties. Thus, it remains unclear how variability in speech production accuracy in early childhood may impact reading development. Therefore, the present longitudinal study seeks to clarify the relation between speech sound production accuracy in kindergarten and subsequent reading outcomes with a focus on additional potential mediating factors. Speech accuracy, core preliteracy skills (phonological awareness, rapid naming, and letter-name knowledge), and additional potential mediators (phonological memory and oral language abilities) were characterized at the start of formal reading instruction. Word reading, decoding, reading fluency, and comprehension were assessed at the end of second grade. Mediation analyses were conducted to examine factors that mediate the relation between speech accuracy in kindergarten and subsequent reading outcomes. Initial associations between early speech sound production accuracy and subsequent reading outcomes were indicated; however, mediation effects of preliteracy skills (phonological awareness and letter-name knowledge) were identified for word reading, decoding, and reading fluency outcomes. For reading comprehension, mediation effects of preliteracy and vocabulary skills were observed. The relation between speech sound production accuracy and subsequent word reading, decoding, reading fluency, and comprehension was observed to be mediated by preliteracy skills, specifically phonological awareness and letter-name knowledge. For reading comprehension only, vocabulary knowledge were of additional importance. https://doi.org/10.23641/asha.23671491.

A Segmented-Edit Error-Correcting Code With Re-Synchronization Function for DNA-Based Storage Systems

As a powerful tool for storing digital information in chemically synthesized molecules, DNA-based data storage has undergone continuous development and received increasingly more attention. Efficiently recovering information from large-scale DNA strands that suffer from insertions, deletions, and substitution errors (collectively referred to as edit errors), is one of the major bottlenecks in DNA-based storage systems. To cope with this challenge, in this paper, we provide a segmented-edit error-correcting code with the re-synchronization function, termed the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DNA-LM</i> code. Compared with the previous segmented-error-correcting codes, it has a systematic structure and does not require the endpoint of the received segment as pre-requisite information for decoding. In the case that the number of edit errors exceeds the edit error-correcting capability of a segment, it can easily regain synchronization to ensure that the subsequent decoding continues. Both encoding and decoding complexity is linear in the codeword length. The redundancy of each segment is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lceil \log k\rceil +6$</tex-math></inline-formula> quaternary symbols, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is the length of the message segment. We further generalize the decoding algorithm to deal with duplicated DNA strands, whereas it still maintains linear time complexity in the codeword length and the number of duplications. Simulations under a stochastic edit errors model show that, at a low raw error rate of the “next-gen” sequencing, our code can enable error-free decoding by concatenating with the (255,223) RS code.

A Unified Approach to Nested and Non-Nested Slots for Spoken Language Understanding

As chatbots become more popular, multi-intent spoken language understanding (SLU) has received unprecedented attention. Multi-intent SLU, which primarily comprises the two subtasks of multiple intent detection (ID) and slot filling (SF), has the potential for widespread implementation. The two primary issues with the current approaches are as follows: (1) They cannot solve the problem of slot nesting; (2) The performance and inference rate of the model are not high enough. To address these issues, we suggest a multi-intent joint model based on global pointers to handle nested and non-nested slots. Firstly, we constructed a multi-dimensional type-slot label interaction network (MTLN) for subsequent intent decoding to enhance the implicit correlation between intents and slots, which allows for more adequate information about each other. Secondly, the global pointer network (GP) was introduced, which not only deals with nested and non-nested slots and slot incoherence but also has a faster inference rate and better performance than the baseline model. On two multi-intent datasets, the proposed model achieves state-of-the-art results on MixATIS with 1.6% improvement of intent Acc, 0.1% improvement of slot F1 values, 3.1% improvement of sentence Acc values, and 1.2%, 1.1% and 4.5% performance improvements on MixSNIPS, respectively. Meanwhile, the inference rate is also improved.

The captivating role of calcium in plant-microbe interaction.

Plant immune response is fascinating due to the complete absence of a humoral system. The adaptive immune response in plants relies on the intracellular orchestration of signalling molecules or intermediates associated with transcriptional reprogramming. Plant disease response phenomena largely depend on pathogen recognition, signal perception, and intracellular signal transduction. The pathogens possess specific pathogen-associated molecular patterns (PAMP) or microbe-associated molecular patterns (MAMP), which are first identified by pattern recognition receptors (PRRs) of host plants for successful infection. After successful pathogen recognition, the defence response is initiated within plants. The first line of non-specific defence response is called PAMP-triggered immunity (PTI), followed by the specific robust signalling is called effector-triggered immunity (ETI). Calcium plays a crucial role in both PTI and ETI. The biphasic induction of reactive oxygen species (ROS) is inevitable in any plant-microbe interaction. Calcium ions play crucial roles in the initial oxidative burst and ROS induction. Different pathogens can induce calcium accumulation in the cytosol ([Ca2+]Cyt), called calcium signatures. These calcium signatures further control the diverse defence-responsive proteins in the intracellular milieu. These calcium signatures then activate calcium-dependent protein kinases (CDPKs), calcium calmodulins (CaMs), calcineurin B-like proteins (CBLs), etc., to impart intricate defence signalling within the cell. Decoding this calcium ionic map is imperative to unveil any plant microbe interplay and modulate defence-responsive pathways. Hence, the present review is unique in developing concepts of calcium signature in plants and their subsequent decoding mechanism. This review also intends to articulate early sensing of calcium oscillation, signalling events, and comprehensive mechanistic roles of calcium within plants during pathogenic ingression. This will accumulate and summarize the exciting roles of calcium ions in plant immunity and provide the foundation for future research.

Minimizing the Average Packet Access Time of the Application Layer for Buffered Instantly Decodable Network Coding

In B-IDNC (buffered instantly decodable network coding), each receiver can cache the non-instantly decodable network coded packets (NIDPs) which contain two wanted packets of the network layer for subsequent network decoding, so that more packets can be recovered at the network layer than the traditional instantly decodable network coding (IDNC). By employing B-IDNC, we consider a radio access network wherein a base station (BS) is required to broadcast a block of packets to a set of receivers. After completing network decoding at the network layer, each receiver can deliver its recovered packets from the network layer to the application layer in order. We consider minimizing the average packet access time of the application layer for B-IDNC. For the optimization problem is intractable, we approximate it to reduce the sum minimum access delay of the application layer across all receivers. The approximate problem is shown to be equivalent to a maximum weight encoding clique problem over the B-IDNC graph. We propose a simple heuristic algorithm based on greedy maximum weight vertex search to solve the approximate problem. Simulation results verify the effectiveness of our proposed algorithm as compared with the existing techniques.

Average Packet Decoding Delay Minimization for Rate-Aware Buffered Instantly Decodable Network Codes

Buffered instantly decodable network coding (B-IDNC) can cache the non-instantly decodable network packets (NIDPs) containing two wanted packets for subsequent network decoding. This letter proposes a cross-layer scheme named as rate-aware B-IDNC (RA-B-IDNC) by jointly considering the B-IDNC decision at the network layer and transmission rate adaption at the physical layer to minimize the average packet decoding delay. Due to the intractability of the considered problem, we approximate it as maximizing the recovery rate which is defined as the ratio of the total number of instantly decoded packets to the transmission time. We design two graphs named as RA-B-IDNC encoding graph which reflects the encoding relations among the vertices of the graph, and RA-B-IDNC decoding graph which indicates the decoding relations among the wanted packets of each receiving node by exploiting its buffered NIDPs. Using these graphs, the approximated problem can be formulated as a maximum weight encoding clique problem. Simulation results demonstrate that RA-B-IDNC can reduce the average packet decoding delay compared with the existing algorithms in the literature.

Square Root Compression and Noise Effects in Digitally Transformed Images

We report on a particular example of noise and data representation interacting to introduce systematic error into scientific measurements. Many instruments collect integer digitized values and apply nonlinear coding, in particular square root coding, to compress the data for transfer or downlink; this can introduce surprising systematic errors when they are decoded for analysis. Square root coding and subsequent decoding typically introduces a variable ±1 count value-dependent systematic bias in the data after reconstitution. This is significant when large numbers of measurements (e.g., image pixels) are averaged together. Using direct modeling of the probability distribution of particular coded values in the presence of instrument noise, one may apply Bayes’ theorem to construct a decoding table that reduces this error source to a very small fraction of a digitizer step; in our example, systematic error from square root coding is reduced by a factor of 20 from 0.23 to 0.012 count rms. The method is suitable both for new experiments such as the upcoming PUNCH mission, and also for post facto application to existing data sets—even if the instrument noise properties are only loosely known. Further, the method does not depend on the specifics of the coding formula, and may be applied to other forms of nonlinear coding or representation of data values.

The relationship between frequency content and representational dynamics in the decoding of neurophysiological data

Decoding of high temporal resolution, stimulus-evoked neurophysiological data is increasingly used to test theories about how the brain processes information. However, a fundamental relationship between the frequency spectra of the neural signal and the subsequent decoding accuracy timecourse is not widely recognised. We show that, in commonly used instantaneous signal decoding paradigms, each sinusoidal component of the evoked response is translated to double its original frequency in the subsequent decoding accuracy timecourses. We therefore recommend, where researchers use instantaneous signal decoding paradigms, that more aggressive low pass filtering is applied with a cut-off at one quarter of the sampling rate, to eliminate representational alias artefacts. However, this does not negate the accompanying interpretational challenges. We show that these can be resolved by decoding paradigms that utilise both a signal's instantaneous magnitude and its local gradient information as features for decoding. On a publicly available MEG dataset, this results in decoding accuracy metrics that are higher, more stable over time, and free of the technical and interpretational challenges previously characterised. We anticipate that a broader awareness of these fundamental relationships will enable stronger interpretations of decoding results by linking them more clearly to the underlying signal characteristics that drive them.

The Superior Colliculus/Lateral Posterior Thalamic Nuclei in Mice Rapidly Transmit Fear Visual Information Through the Theta Frequency Band

Animals perceive threat information mainly from vision, and the subcortical visual pathway plays a critical role in the rapid processing of fear visual information. The superior colliculus (SC) and lateral posterior (LP) nuclei of the thalamus are key components of the subcortical visual pathway; however, how animals encode and transmit fear visual information is unclear. To evaluate the response characteristics of neurons in SC and LP thalamic nuclei under fear visual stimuli, extracellular action potentials (spikes) and local field potential (LFP) signals were recorded under looming and dimming visual stimuli. The results showed that both SC and LP thalamic nuclei were strongly responsive to looming visual stimuli but not sensitive to dimming visual stimuli. Under the looming visual stimulus, the theta (θ) frequency bands of both nuclei showed obvious oscillations, which markedly enhanced the synchronization between neurons. The functional network characteristics also indicated that the network connection density and information transmission efficiency were higher under fear visual stimuli. These findings suggest that both SC and LP thalamic nuclei can effectively identify threatening fear visual information and rapidly transmit it between nuclei through the θ frequency band. This discovery can provide a basis for subsequent coding and decoding studies in the subcortical visual pathways.

Research on user granularity-level personalized social text generation technology

With the introduction of large-scale pre-trained language models, breakthroughs have been made in text generation technology research. On this basis, in order to assist users to complete personalized creation, this paper proposes a user-level fine-grained control generation model. First, we design the Encoder-Decoder framework based on the GPT2 structure, and model and encode the user’s static personalized information on the Encoder side. Then add a bidirectional independent attention module to receive the personalized feature vector on the Decoder side. The attention module in the original GPT2 structure captures the dynamic personalized features in the user text, namely writing style, expression way, etc. Next, the scores of each attention module are weighted and fused to participate in the subsequent decoding to automatically generate social texts constrained by the user’s personalized feature attributes. However, the semantic sparsity of the user’s basic information will cause occasional conflicts between the generated text and some personalized features. Therefore, we use the Alignment module to perform the secondary enhancement and generation of consistent understanding between the output data of the Decoder and the user’s personalized features, and finally realize the personalized social text generation. Experiments show that compared with the GPT2 baseline model, the fluency of the model is improved by 0.3%-0.6%, and on the basis of no loss of language fluency, the social text generated by the model can have significant user personalization characteristics, among which personalization and consistency the two evaluation indicators of sexuality both increased significantly by 8.4% and 9%.

Maximum Decoding Clique Based Maximum Weight Vertex Search Algorithm for Buffered Instantly Decodable Network Codes

The traditional instantly decodable network coding (IDNC) schemes discard the non-instantly decodable packets (NIDPs), which results in the loss of useful information. In this letter, we consider a network coding scheme named as buffered instantly decodable network coding (B-IDNC) where each receiving node can cache the NIDPs containing two unreceived packets for subsequent network decoding, and give a decoding algorithm by utilizing the maximum decoding clique. The maximum decoding clique is defined as a subset of vertices in the IDNC graph, which can be decoded together. We approximate the average packet decoding delay minimization problem as the maximum weight encoding clique problem, and a heuristic algorithm named as maximum decoding clique based maximum weight vertex (MDC-MWV) searching algorithm is proposed to solve the approximated problem. Simulation results show that our proposed algorithm can reduce the average packet decoding delay compared with the conventional IDNC schemes.

Lattice-Based Minimum-Distortion Data Hiding

Lattices have been conceived as a powerful tool for data hiding. While conventional studies and applications focus on achieving the optimal robustness versus distortion tradeoff, in some applications such as data hiding in medical/physiological signals, the primary concern is to achieve a minimum amount of distortion to the cover signal. In this paper, we revisit the celebrated quantization index modulation (QIM) scheme and propose a minimum-distortion version of it, referred to as MD-QIM. The crux of MD-QIM is to move the data point to only the boundary of the Voronoi region of the lattice point indexed by a message, which suffices for subsequent correct decoding. At any fixed code rate, the scheme achieves the minimum amount of distortion by sacrificing the robustness to the additive white Gaussian noise (AWGN) attacks. Simulation results confirm that our scheme significantly outperforms QIM in terms of mean square error (MSE), peak signal to noise ratio (PSNR) and percentage residual difference (PRD).