Interpretation of zeroinfl Estimate

I am using zeroinfl on my dataset. However, I am confused by Estimate, as zeroinfl reports Estimate in different directions. Can Estimate be used as the relationship between y and x?

########################################### zeroinfl(formula = y ~ x, data = df2, dist = "poisson")

Pearson residuals: Min 1Q Median 3Q Max -0.9592 -0.1067 -0.1067 -0.1067 31.6334

Count model coefficients (poisson with log link): Estimate Std. Error z value Pr(>|z|) (Intercept) 0.08631 0.09120 0.946 0.344 x -0.03053 0.04449 -0.686 0.493

Zero-inflation model coefficients (binomial with logit link): Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.81373 0.09915 38.47 <2e-16 x -1.70795 0.13611 -12.55 <2e-16

Signif. codes: 0 '' 0.001 '' 0.01 '' 0.05 '.' 0.1 ' ' 1

Number of iterations in BFGS optimization: 9 Log-likelihood: -1233 on 4 Df

############################# base = glm(y ~ x, family = 'poisson', data = df2)

glm(formula = y ~ x, family = "poisson", data = df2)

Deviance Residuals: Min 1Q Median 3Q Max
-3.4023 -0.2437 -0.2437 -0.2437 7.9080

Coefficients: Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.51675 0.05626 -62.51 <2e-16 x 0.75321 0.02516 29.94 <2e-16

Signif. codes: 0 '' 0.001 '' 0.01 '' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for poisson family taken to be 1)

Null deviance: 3084.2  on 10248  degrees of freedom

Residual deviance: 2703.1 on 10247 degrees of freedom AIC: 3252.5

atahk / pscl