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## مدل سازی اعوجاج انتها به انتها و بهینه سازی منطبق با کانال برای طرح کدگذاری چند توصیفه MLMDC | ||

هوش محاسباتی در مهندسی برق | ||

مقاله 4، دوره 12، شماره 4، دی 1400، صفحه 31-42 اصل مقاله (998.55 K)
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نوع مقاله: مقاله پژوهشی انگلیسی | ||

شناسه دیجیتال (DOI): 10.22108/isee.2019.118706.1266 | ||

نویسنده | ||

محمد کاظمی^{*}
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^{}گروه مهندسی برق- دانشکده فنی و مهندسی- دانشگاه اصفهان- اصفهان- ایران | ||

چکیده | ||

-- | ||

کلیدواژهها | ||

Video Transmission؛ Multiple Description Coding؛ End-to-end Distortion Optimization؛ Error Propagation | ||

اصل مقاله | ||

Introduction Mixed Layer MDC (MLMDC) At the side decoder, the mentioned separation is not possible, since one description is not available. Here, the base coefficients are estimated from the combined coefficients, as given by where Z_0 and N_0 are functions of λ , Q and c; their functionality and the derivation details can be found in [19]. Consequently the side distortion is computed by MLMDC with the above structure is an MDC scheme shows promising performance especially at high Packet Loss Rates (PLRs). End-to-end Distortion Model for MLMDC where D_c^n and D_Q^n are channel and quantization distortions, respectively; Δ_X^i is the mismatch distortion and is defined as: in which δ_X^i is the mismatch between the transmitted signal and received signal for the ith frame; and δ_Q^n is the difference signal caused by quantization at the encoder. For the channel distortion we can write that: where β^j is the intra rates of the jth frame. Equation (6) says that in order to find the channel distortion at each frame, one must calculate the mismatch distortion associated with that frame as well as the mismatch distortion of all previously coded frames. in which the mismatch distortion associated with the nth frame, Δ_(X_(2L-est))^n, which is due to the current frame data loss and not due to the error propagation, has been separated. Correspondingly, (4) becomes: E[(δ_(X_(2L-est) )^n )^2 ]+E[(δ_(Q )^n )^2 ]=2P_01 E[(δ_01^i )^2 ]+E[(δ_(Q )^n )^2 ]=2P_01 E[(X ̂_0^n-X ̂_1^n )^2 ]+E[(X^n-X ̂_0^n )^2 ] (10) 2P_01 E[(X ̂_0^n-X ̂_1^n )^2 ]+E[(X^n-X ̂_0^n )^2 ]=(2P_01 )(E[(X ̂_0^n-X ̂_1^n )^2 ]+E[(X^n-X ̂_0^n )^2 ])+(1-2P_01 )E[(X^n-X ̂_0^n )^2 ] (11) With the assumption of independency between channel and quantization distortions [10], (10) becomes: E[(δ_(X_(2L-est) )^n )^2 ]+E[(δ_(Q )^n )^2 ]=2P_01 E[(X^n-X ̂_1^n )^2 ]+(1-2P_01 )E[(X^n-X ̂_0^n )^2 ]=2P_01 D_side^n+(1-2P_01 ) D_cen^n (12) where D_side^n and D_cen^n are given by (3) and (1), respectively. Using (12), the distortion of (9) becomes: In equation above only E[(δ_(X_(1L-est) )^i )^2 ] is unknown which can be computed as follows: Objective Function It shows that the solution of equation (18), {C_λ^* ,QP_λ^*}, is the solution of the constrained problem of (16) but with the constraint ∑_(n=0)^(N-1)▒(R_1^n+R_2^n ) <R(λ), as shown in [22]. In other words, if λ^* is found such that R(λ^* )= R_t, the solutions of (18) and the solutions of (16) are identical. The distribution parameter of DCT coefficients of the GOP under optimization is calculated. Experimental Results The parameters are chosen such that the first frame of the GOP (I-frame) is duplicated in both descriptions and for the next P-frames, a constant MDC parameter is used.
Optimization and RD Curves
Conclusion | ||

مراجع | ||

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