## 2 Related Work  
The estimation of an accurate CNM between the correlated sources either at encoder or decoder with fully unknown or partially known data [16; 17] is a key problem in DVC. Therefore, a lot of literature is devoted to the estimation of the CNM, but most of them follow the unrealistic offline assumption [11]. In DVC, the overall coding efficiency and RD performance of codec improved with an accurate estimation of parameters of the CNM. In the DVC codec, neither WZF nor the estimated SI of corresponding WZF is available at the decoder and encoder, respectively. Therefore, offline approaches are adopted to calculate the CNM parameter through the distribution model by assuming either SI is available at the encoder or the actual WZF is available at the decoder. In this approach, the parameter is calculated offline for the whole video sequence and later used in the decoder. This is one of the biggest hurdles in the practical implementation of DVC because offline estimation of the CNM does not perform well for all types of motion videos. Furthermore, the correlation noise is not stationary, and its statistics vary from frame to frame. Therefore, the estimation of CNM parameters for different sequences through the offline CNM is not feasible to achieve coding efficiency and high RD performance. This is because when it is implemented at the decoder, it does not exploit the variability of CNM from frame to frame for actual motion. Furthermore, this is a complex task since the original information is not available in the decoder, and the SI quality varies throughout the sequence. However, if the model accurately describes the WZF and SI relationships, the coding performance is high and vice-versa. A Laplacian distribution model is applied in most architectures because of its excellent trade-off between model accuracy and complexity. Hence, the focus has been put on online CNM parameter estimation without having access to the original WZF. The authors of [18] proposed several online correlation noise schemes for pixel-based coding at different granularity levels (frame, block, and pixel) by exploiting the temporal correlation between the decoded KF and estimated SI. In an extended work [19], the online CNM is adopted for the transform domain at the band level and coefficient level. Enhancements were made in [20] to the codec in [19] with the introduction of cross-band correlation by calculating residuals between bands, and a band classification map, which is updated after the successful decoding of each band. With cross-band correlation, the classification map of the current band is used to estimate the classification map of the next band. With this estimated cross-band classification of each band, the CNM parameter is calculated. In [21], the correlation noise parameter estimation is done at the DCT band level, where SI is refined after the decoding of each bit plane. Therefore, the authors attempted to refine the CNM as well. The authors in [22] put efforts into controlling the rate without a feedback channel. Based on the motion intensity, the algorithm adjusted the rates by switching the Laplacian distribution parameters in CNM between frame and block levels. The authors in [23] proposed an adaptive low computational DVC, which estimated correlation by using the expectation propagation during the channel decoding process. The correlation estimation is carried out jointly with decoding of the factor graph during the channel decoding process. In [24], the authors proposed a CNM that is independently executed at both the encoder and decoder at no cost of extra computation at the encoder. At the encoder, the CNM calculates the number of least significant bits that are required to be sent to the decoder, assuming that the remainder is determined at the decoder. In [25], the authors proposed a hash-based DVC where the correlation noise is statistically dependent on the SI. The proposed algorithm performs online estimation of the SI-dependent correlation noise parameter at the transform coefficient bands level.