## 1 Introduction  
Many upstream applications of wireless video sensor networks (WVSN), such as multimedia sensor networks, real-time wireless video surveillance, environment monitoring, medical monitoring, and internet of things (IoT), have emerged over recent time [1, 2, 3, 4]. The battery size and life and other limitations of the transmitting device are of paramount importance in such applications [5]. For such low-resource upstream applications, low computational complexity at the encoder is desirable [6]. One of the video coding paradigms that support these requirements is distributed video coding (DVC) [7]. This coding paradigm redistributes the coding complexity and shifts most of the computation to the decoder by exploiting the source statistics at the decoder [8] and making the encoder computationally light [9]. However, this new coding design is not fully developed and is getting more attention from the research community to improve its performance compared with conventional codecs for real-time and resource-constrained applications [10, 11]. DVC is based on the Slepian-Wolf (SW) [12] and Wyner-Ziv (WZ) [13] theorems of Information Theory. According to the SW theorem, if two correlated sources are encoded separately and decoded jointly, then the minimum coding rate we can achieve is the same as that of joint encoding and decoding. [PERSON] and Ziv obtained similar bounds for lossy coding with the presence of decoder side information (SI). The two major categories of DVC are block-based and frame-based DVC frameworks. Most of the available DVC codecs in literature are based on the frame-based Stanford architecture [14], which can then be divided into two types--pixel domain WZ (PDWZ) coding and transform domain WZ (TDWZ) coding. TDWZ coding is widely used in the literature due to its coding efficiency, and the DISCOVER codec (DIS) [10] is considered the state-of-the-art codec based on this architecture [14]. In TDWZ DVC schemes, the video is split into frames, and these frames are organized into Group of Pictures (GOP) of sizes 2, 4, or 8. For every GOP, the first frame is the key-frame (KF) and encoded with intra-predictive coding (H.264 intra-encoder). The rest of the GOP frames, called Wyner-Ziv frames (WZFs), are encoded with WZ coding. The WZF is first split into 4 \(\times\) 4 blocks, and discrete cosine transform (DCT) is applied on each block of the frame. The corresponding coefficients from each block are organized into 16 coefficient bands. Each coefficient band is quantized to the desired quality. For each band, the bit-planes are extracted after quantization and channel encoded (turbo or LDPC) for parity bit generation. These parity bits are stored in a buffer and transmitted on the decoder's request through a feedback channel. At the decoder, the first KF is decoded. Then the SI, which is an approximated replica of the current WZF, is estimated with motion-compensated interpolation or extrapolation of the previous and subsequent decoded frames. One challenge in DVC is to find a channel noise model (CNM) that accurately models the statistical dependency in the form of virtual channel correlation noise, between the current WZF and its corresponding SI. A more accurate model will lead to improved performance in coding efficiency and rate distortion (RD). In our previous research work [15], efforts were made to develop a phase-based frame interpolation algorithm that can effectively generate the SI faster and with lower computational complexity compared to the baseline codec, DIS. In this paper, however, our main objective is to design an online CNM framework that can be used to develop a full DVC codec that has less decoding computational complexity and can compete with DIS in coding efficiency and RD performance. In most DVC codecs, the computation of error distribution between the original WZF and corresponding SI is challenging due to the unavailability of SI at the encoder and the original WZF at the decoder.