Are you sure you are using the correct pricing formula.
For a binary (digital) call that pays $1$, the simple Black-Scholes price at time $t=0$ is
$$ C_d = e^{-rT}N(d_2)$$
$$d_2 = \frac{\text{ln}(F/K) - \frac1{2}\sigma^2T}{\sigma \sqrt{T}}$$
where $N$ is the standard normal distribution function, $F=Se^{(r-q)T}$ is the forward index price, $S$ is the spot index price, $K$ is the strike price, $T$ is the time to expiration and $\sigma$ is the implied volatility.
Here are some current values
$$S = 1942, r = 0.25\%, q = 1.97\%.$$
For the 14 Aug 1950 call
$$K = 1950, T = 64 \ \text{days}=0.1752 \ \text{years} ,$$
and assuming an implied volatility of $\sigma = 12\%$, the binary call price is $0.43$.
So the quotes you are showing look reasonable under the current implied volatility conditions.
In terms of your general question about finding implied volatility, there are two issues. (1) How to build a no-arbitrage pricing model that will correctly match market prices of vanilla calls and puts, and (2) how to price more exotic options (such as binary options) in the new framework.
In general, observed market prices of SPX index options are not consistent with simple Black-Scholes assumptions -- an underlying that follows geometric Brownian motion with constant volatility. The actual prices look like expecations under a probability distribution that is not lognormal -- perhaps more skewed. Implied volatility -- that value which makes the Black-Scholes formula match the market price -- varies both with strike price and time to expiration. In theory, if we knew the market price of a call option $C(S,t;K,T)$ for every conceivable strike price $K$ when the index price is $S$ at time $t$, then for a given time-to-expiration $T$ we could find the implied probability density function as
$$f(S) = e^{r(T-t)}\frac{\partial^2}{\partial K^2}C(S,t;K,T).$$
In practice, there are not enough market price observations to use this formula directly in a meaningful way -- but it suggests there are broader stochastic models (with more degrees of freedom) that can be used to generate no-arbitrage option prices that match market prices. One of the more popular approaches is the local volatility model that assumes the underlying index price follows a stochastic process of the form
$$dS_t=\mu S_t dt + \sigma(S_t)S_tdW_t$$
where $W_t$ is a Brownian motion and the volatility $\sigma(\cdot)$ is not a constant but a deterministic function of the underlying price. There is an extensive literature on the local volatility model indicating how to calibrate the function $\sigma(\cdot)$ to match market prices.
For a binary option, it is not entirely clear what simple pricing appoach should be used when vanilla calls and puts exhibit an implied volatility skew. One possibility is to find the price in terms of a replicating portfolio of vanilla options. If a binary option pays $1$ when the index is above a strike price $K$ then it can be replicated, in theory,approximately using a call spread. We would buy a number $1/\delta$ of ordinary calls with strike price $K$ and sell the same number of calls with strike price $K+\delta.$ In this way
$$C_d(S,t;K,T) \approx \frac1{\delta} \big[C(S,t;K,T)-C(S,t;K+\delta , T)\big]$$
Ideally we would make $\delta$ as small as possible, but there are practical limitations in terms of available strikes and the eventual leverage that would be applied. Nevertheless, this replication model suggests how the binary option might be priced in the presence of a volatility skew. Taking the limit as $\delta \rightarrow 0$ we get
$$C_d(S,t;K,T) \approx \frac{\partial}{\partial K} C(S,t;K,T),$$
and this relationship indicates how to extract the price of the binary option that is consistent with the prices of vanilla options in a framework (eg. local volatility) where implied volatility depends on strike.